Abstract
With the development of network technology, the importance of intrusion detection has gradually increased. At the same time, due to the continuous increase in the number of network connections, the efficiency of traditional intrusion detection technologies is low. In order to solve this problem, this article uses GA-XGB algorithm for intrusion detection. The model uses genetic algorithm for feature selection, remove redundant and low-relevant features and XGBoost algorithm for final classification. Experiments conducted with the KDD data set prove that the accuracy, recall, F1 score and ROC score of the GA-XGB algorithm are improved compared to other traditional machine learning algorithms.
X. Wen−Project Fund: Research Project of Fundamental Scientific Research Business Expenses of Provincial Colleges and Universities in Hebei Province 2021QNJS04.
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Zhao, R., Mu, Y., Wen, X. (2021). Intrusion Detection Based on GA-XGB Algorithm. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_14
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DOI: https://doi.org/10.1007/978-981-16-7476-1_14
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