基于堆稀疏自编码的二叉树集成入侵检测方法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-04-01 Cooperative journals: 《计算机应用研究》

柳毅 阴梓然 洪洲

Abstract： So far, many different machine learning methods have been proposed, and traditional machine learning methods can not effectively solve the classification problem of large-scale intrusion data. 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, the category labels were divided into five categories and constructed into binary tree structures, then the imbalance of data distribution was solved by the upper sampling method, the above processing could separate the large-scale data, so that they could be trained separately, and then, the sparse autoencoder network was used to reduce the feature dimension. Using this method could ensure that time of dimension reduction could be saved on the basis of extracting deeper features from the original data. Finally, the lightGBM ensemble algorithm was used to classify. And compared to other models, using the lightGBM model could save training time while ensuring classification performance. The NSL-KDD dataset was used to measure the accuracy, accuracy, 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.

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