An SSA-LC-DAE Method for Extracting Network Security Elements

Abstract

In the current network environment, the original network data present the characteristics of multiple features and high dimensions. Furthermore, in the existing element extraction methods based on deep neural networks, the loss of feature information layer by layer increases as the data dimensions decrease, which greatly affects the retention of network data information elements and brings a huge challenge to effective network security protection. This paper refers to a residual neural network to improve the deep autoencoder (DAE) and then utilizes them to propose a novel element extraction method named the layer-by-layer loss compensation deep autoencoder (LC-DAE) based on the sparrow search algorithm (SSA). In the proposed method, a loss compensation module is added to each encoding layer of the DAE. Specifically, this module first restores the data by using the decoding layer corresponding to the encoding layer. Then, the loss value of the calculated characteristic information is compensated using the output of the corresponding encoding layer. Subsequently, the SSA is used to optimize the LC-DAE in the training process. Finally, the experimental results show that compared with the existing methods, this method retains more sufficient element information, and significantly improves the classification performance of data using neural networks.