Intrusion Detection Model Based on Deep Learning and R-Drop Regularization
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.
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