<p>Intrusion Detection System (IDS) plays as a role in detecting various types of attacks on computer networks. IDS identifies attacks based on a classification data network. The result of accuracy was weak in past research. To solve this problem, this research proposes using a wrapper feature selection method to improve accuracy detection. Wrapper-Feature selection works in the preprocessing stage to eliminate features. Then it will be clustering using a semi-supervised method. The semi-supervised method divided into two steps. There are supervised random forest and unsupervised using Kmeans. The results of each supervised and unsupervised will be ensembling using linear and logistic regression. The combination of wrapper and semi-supervised will get the maximum result.</p>
Differential Evolution Wrapper Feature Selection for Intrusion Detection System
