Build intrusion detection model based on CNN and ensemble learning

In order to effectively detect network attacks, machine learning is widely used to classify different types of intrusion detection. Traditional detection usually used a single model to train data, which was prone to the problems of large generalization error and over fitting. In order to solve this problem, the idea of ensemble learning is introduced, and an intrusion detection model based on CNN and ensemble learning is proposed. Firstly, CNN is used to mine the deep information in the original data, and then the mined information is taken as the input and detected by using the ensemble learning model. According to the stacking strategy, a variety of heterogeneous models are used as the base-learner, and support vector machine is selected as the meta-learner. The NSL-KDD dataset is used to train and test the intrusion detection model. The experimental results show that the model can obtain higher accuracy and has very good intrusion detection classification effect.