Intrusion Detection Algorithm Based on Convolutional Neural Network and Light Gradient Boosting Machine

Aiming at the limitations of existing algorithms of network intrusion detection in dealing with complex data of imbalance and high dimensionality, this paper proposes an intrusion detection algorithm based on convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM). First, the data-type conversion, oversampling technology and image data conversion are included in the data preprocessing to make the data balanced and adapt to the input format. Then, by the convolutional layer, pooling layer and fully connected layer of the CNN model, the main features are abstracted from the converted image data. Finally, data of the main features is used for training and testing the LightGBM model, so as to get the final classification results. This paper uses KDDCUP99 dataset to carry out multi-classification experiments. By comparing the experiments before and after balancing the dataset, and comparing with similar algorithms, it verifies the superiority of the proposed algorithm in the classification performance of intrusion detection, especially for the minority attack classes.