计算机与数字工程2024，Vol.52Issue(4)：1142-1148,7.DOI:10.3969/j.issn.1672-9722.2024.04.032
基于深度学习和R-Drop正则的入侵检测模型
Intrusion Detection Model Based on Deep Learning and R-Drop Regularization
李为 1程相鑫1
作者信息
- 1. 华北电力大学控制与计算机工程学院 北京 102206
- 折叠
摘要
Abstract
In the task of intrusion detection classification,the performance of traditional machine learning models often can not achieve good results,and the generalization ability of deep learning technology is stronger.Therefore,it is of great significance to study deep learning algorithm and apply it to the intrusion detection system.After research,aiming at the problem of network traf-fic 2 classification,this paper proposes a classification model based on FNet,it is TFN.Aiming at the problem of multi-classifica-tion of network traffic,a deep learning multi-classification model based on R-Drop regularization is proposed.This paper uses the intrusion detection data set NSL-KDD as the experimental data.The experimental results show that the proposed 2 classification model has an excellent effect and accuracy of 99.99%on the NSL-KDD data set.The proposed multi-classification method also im-proves the accuracy by 1%～2%compared with the ordinary training method.关键词
入侵检测/深度学习/正则Key words
intrusion detection/deep learning/regularization分类
信息技术与安全科学引用本文复制引用
李为,程相鑫..基于深度学习和R-Drop正则的入侵检测模型[J].计算机与数字工程,2024,52(4):1142-1148,7.
