基于堆稀疏自编码的二叉树集成入侵检测方法.

In order to solve the problem of classification of large-scale intrusion data, this paper proposed lightGBM binary tree algorithm based on stacked sparse autoencoder. Firstly, it divided the category labels into five categories and constructed into binary tree structures. Then solved the imbalance of data distribution by the upper sampling method, the above processing could separate the large-scale data, so that they could be trained separately. Next, it used the sparse autoencoder network to reduce the feature dimension. Using this method could ensure that time of dimension reduction could save on the basis of extracting deeper features from the original data. Finally, it used the lightGBM ensemble algorithm to classify. And compared to other models, using the lightGBM model could save training time while ensuring classification performance. It used the NSL-KDD dataset to measure the accuracy, precision, recall. And comprehensive evaluation index F1 of the proposed method, which reached an average of 87. 42%, 98. 20% and 91. 31% in five classification, respectively. It is superior to the comparison algorithm and obviously saves the calculation time . [ABSTRACT FROM AUTHOR]