Gradient importance enhancement based feature fusion intrusion detection technique

Abstract

In order to better deal with the current unstable network security situation and improve the accuracy and generalization ability of the intrusion detection model, this paper proposes a feature fusion technique based on gradient importance enhancement, which combines feature fusion and feature enhancement to increase the diversity of sample features, so that the model can focus more on the sample features related to classification, making the model more generalizable and improving the accuracy of the model. The final model was experimented on two datasets, NSL-KDD and CICIDS2017, and its accuracy reached 99.84% and 99.78%, respectively.