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
在入侵检测分类任务中,传统的机器学习模型其性能往往不能达到较好的效果,深度学习技术泛化能力更强,因此研究深度学习算法并应用到入侵检测检测系统中是有十分有意义的.论文经过研究,针对网络流量2分类问题,提出了一种基于FNet分类模型——TFN;针对网络流量多分类问题,提出了一种基于R-Drop正则的深度学习多分类模型.论文使用入侵检测数据集NSL-KDD作为实验数据,实验结果表明,在NSL-KDD的数据集上,提出的2分类模型效果优异,准确度达到99%;而提出的多分类方法,与普通训练方法相比也提升了1%～2%的准确度.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据