An intrusion detection model using smote and ensemble learning

As a security defense technology to protect the network from attack, network intrusion detection system plays a very important role in the field of computer system and network security. Aiming at the multi classification problem of unbalanced data in network intrusion detection, machine learning has been widely used in intrusion detection, which is more intelligent and accurate than traditional methods. The existing multi classification methods of network intrusion detection are improved, and an intrusion detection model using smote and ensemble learning is proposed. The model is mainly divided into two parts: smote oversampling and stacking classifier. The NSL-KDD dataset is used to test the Stacked Ensemble model in this paper. Compared with the other five basic learner models, the Stacked Ensemble has a higher detection rate. Stacked Ensemble has significant advantages in solving the multi classification problem of unbalanced network intrusion detection data. It is a practical and feasible intrusion detection method.