Intrusion Detection Model Based on Multi‐Kernel Approximation and Deep Neural Network

Aiming to address the problems of low time efficiency and poor generalization ability in support vector machine (SVM) models when dealing with large‐scale network intrusions, this paper suggests a large‐scale robust intrusion detection model that combines deep neural network (DNN) and multi‐kernel approximate SVM. The DNN conducts representation learning to extract intrinsic features and performs dimensionality reduction on the dataset. The paper leverages the capability of multi‐kernel learning to accommodate the distinct characteristics of various features within the input space. Additionally, it aims to further improve the robustness of the model. The multi‐kernel approximation SVM using random Fourier features to perform kernel approximation can handle large‐scale datasets. The model employs the gradient descent method to train neural networks and multi‐kernel SVM from start to finish. The gradient descent algorithm can effectively maximize convergence toward the global minimum. This, in turn, enhances the overall accuracy of the model. Our model was tested on three intrusion detection datasets of varying scales: UNSW‐NB15, CIC‐IDS2017, and CIC‐IDS2018; and compared with the latest learning models such as gradient boosting tree, CNN, LSTM, GNN, transfer learning, and SVM models of different variants. The accuracy rate of the model proposed in this paper has increased by 1%–4% compared with that of the currently popular intrusion detection models. The experimental findings show that our model has higher classification performance and better robustness when processing large‐scale datasets while reducing time complexity.