Based ontwice decision for deep learning intrusion detection model

JIANG Ze-tao; ZHAI Zhen-yu

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Based ontwice decision for deep learning intrusion detection model

更新时间：2026-09-04

- Based ontwice decision for deep learning intrusion detection model
- Vol. 37, Issue 4, Pages: 32-36(2020)
- 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：
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- Published：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

HINTON G E, OSINDERO S, TEH Y W. A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation, 2006, 18(7):1527-1554. DOI: 1527

GAO N, GAO L, GA Q, et al. An intrusion detection model based on deep belief networks[C]//2014 Second International Conference on Advanced Cloud and Big Data. IEEE, 2014: 247-252.

YU Y, LONG J, LIU F, et al. Machine learning combining with visualization for intrusion detection: a survey[C]//International Conference on Modeling Decisions for Artificial Intelligence. Springer, Cham, 2016: 239-249.

YU Y, LONG J, CAI Z. Network intrusion detection through stacking dilated convolutional autoencoders[J]. Security and Communication Networks, 2017: 1-10. DOI: 1

AUNG Y Y, MIN M M. Hybrid Intrusion Detection System Using K-Means and Classification and Regression Trees Algorithms[C]//2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA). IEEE, 2018: 195-199.

ZHENG K, CAI Z, ZHANG X, et al. Algorithms to speedup pattern matching for network intrusion detection systems[J]. Computer Communications, 2015, 62:47-58. DOI: 47

CAI Z, WANG Z, ZHENG K, et al. A Distributed TCAM Coprocessor Architecture for Integrated Longest Prefix Matching, Policy Filtering, and Content Filtering[J]. IEEE Transactions on Computers, 2013, 62(3):417-427. DOI: 417

Hongwei D, Liang W. Research on Intrusion Detection Based on KPCA-BP Neural Network[C]//2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE, 2018: 911-915.

SOMMER R, PAXSON V. Outside the closed world: On using machine learning for network intrusion detection[C]//2010 IEEE symposium on security and privacy. IEEE, 2010: 305-316.

VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion[J]. Journal of Machine Learning Research, 2010, 11(12):3371-3408. DOI: 3371

WU S X, BANZHAF W. The use of computational intelligence in intrusion detection systems: A review[J]. Applied soft computing, 2010, 10(1): 1-35. DOI: 1

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