信息工程大学学报2025，Vol.26Issue(5)：554-560,7.DOI:10.3969/j.issn.1671-0673.2025.05.008
结合数据平衡和深度卷积神经网络的入侵检测方法
Intrusion Detection Method Combining Data Balancing and Deep Convolutional Neural Networks
连盛 1江刚武 1杨宇 2麻顺顺1
作者信息
- 1. 信息工程大学,河南 郑州 450001
- 2. 武警工程大学 信息学院,陕西 西安 710086
- 折叠
摘要
Abstract
In response to the problems of traditional intrusion detection methods for high-dimensional traffic data,such as data imbalance,low feature extraction efficiency,and difficulty in convergence dur-ing training,an intrusion detection method based on deep convolutional neural networks combined with data balancing is proposed.Firstly,the CICIDS-2018 network traffic dataset is transformed into grayscale images with integer values,which are then input into a conditional generative adversarial net-work.Secondly,the trained generator is used to produce attack data for the minority class,which is added to the original dataset to balance it.Finally,the performance of intrusion detection is enhanced by using deep convolutional neural network.Experimental results show that the accuracy rate in multi-classification task of this method is 96.58%,which is superior to that of traditional detection methods,with the classification accuracy rates for the two least frequent attack traffic data Botnet and SQL in-creased by 5.83%and 32.18%,respectively,compared to those before balancing.关键词
网络入侵检测/数据集平衡/生成对抗网络/卷积神经网络Key words
network intrusion detection/dataset balancing/generative adversarial network/convolu-tional neural network分类
计算机与自动化引用本文复制引用
连盛,江刚武,杨宇,麻顺顺..结合数据平衡和深度卷积神经网络的入侵检测方法[J].信息工程大学学报,2025,26(5):554-560,7.
