FCNN-SE: An Intrusion Detection Model Based on a Fusion CNN and Stacked Ensemble

Abstract

As a security defense technique to protect networks from attacks, a network intrusion detection model plays a crucial role in the security of computer systems and networks. Aiming at the shortcomings of a complex feature extraction process and insufficient information extraction of the existing intrusion detection models, an intrusion detection model named the FCNN-SE, which uses the fusion convolutional neural network (FCNN) for feature extraction and stacked ensemble (SE) for classification, is proposed in this paper. The proposed model mainly includes two parts, feature extraction and feature classification. Multi-dimensional features of traffic data are first extracted using convolutional neural networks of different dimensions and then fused into a network traffic dataset. The heterogeneous base learners are combined and used as a classifier, and the obtained network traffic dataset is fed to the classifier for final classification. The comprehensive performance of the proposed model is verified through experiments, and experimental results are evaluated using a comprehensive performance evaluation method based on the radar chart method. The comparison results on the NSL-KDD dataset show that the proposed FCNN-SE has the highest overall performance among all compared models, and a more balanced performance than the other models.