Ensemble Feature Selection for Improving Intrusion Detection Classification Accuracy

In network security, intrusion detection plays an important role. Feature subsets obtained by different feature selection methods will lead to different accuracy of intrusion detection. Using individual feature selection method can be unstable in different intrusion detection scenarios. In this paper, the idea of ensemble is applied to feature selection to adjust feature subsets. Feature selection is converted into a two-category problem, and odd number of feature selection methods is used for voting method to decide whether a feature is required or discarded. In actual operation, mean decrease impurity, random forest classifier, stability selection, recursive feature elimination and chi-square test are used. Feature subsets obtained from them will be adjusted by our proposed method to get ensemble feature subsets. To test the performance, support vector machine, decision tree, knn and multi-layer perception are used to observe and compare the classification accuracy with ensemble feature subsets. Three intrusion detection data sets, including kddcup99, cidds-001 and unsw_nb15 are used in our experiments. The best result is reflected on cidds-001 with a 99.40% classification accuracy. The investigation shows that our method has a certain improvement on classification accuracy of intrusion detection.