An Analysis of the Application of Machine Learning in Network Security
DOI:
https://doi.org/10.5281/zenodo.10775504Keywords:
Data Imbalance, SMOTE, GA-XGBoost, Network Data ClassificationAbstract
In order to deal with the problem of imbalance and complex feature relationship in network data classification, this study proposes a machine learning classification method, combined with improved SMOTE technology and genetic algorithm optimization for XGBoost (GA-XGBoost). Sample balance is achieved by introducing local outlier factors in the SMOTE process, oversampling the minority classes, and randomly undersampling the majority classes. Meanwhile, the evolutionary iterative advantage of the genetic algorithm was used to optimize the XGBoost model parameters and improve the model fit. The experimental results on the UNSW _ NB 15 dataset show that the classification prediction accuracy reached 97.40%, average recall of 70.2% and average F1-score of 68.8%, showing a higher performance compared to the traditional machine learning algorithm and SMOTE + XGBoost method. In addition, the tests on the data collected by the industrial information security platform also confirmed the effectiveness of the proposed method, with a classification accuracy of 99%.
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