A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection

Abstract

In network intrusion detection, using a machine learning method alone has blind spots and low detection accuracy. A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection is proposed. Firstly, the convolution neural network is used to extract the deep information in the original data set, which is normalized as the input of the model. In constructing base classifiers, different heterogeneous model combinations are used to enhance the diversity of base classifiers. Experiments on NSL-KDD dataset show that the proposed model can comprehensively improve the detection accuracy, accuracy, recall and F1-score.