Analysis of training of deep neural networks with heterogeneous architecture while detecting malicious network traffic

Abstract

The given article presents the study of the training process of a composite heterogeneous neural network with deep architecture. A brief description of the architecture of the analyzed neural network system has been given. The key nodes of computational load distribution on each of the layers of the neural network have been tracked. The monitoring results have been analyzed and the following conclusions have been made: the most resource-intensive part of the system during training is the LSTM network. The results of the study of structural features of the neural network hidden layers have been presented. Graphs have been constructed and there has been carried out the study of a statistical distribution of weights on each unique layer of the architecture: on a fully connected layer, on the first hidden layer of the subnet-encoder, on the second hidden layer of the subnet-encoder, on the first and second fully connected output layers of the neural network. Based on the research results, a qualitative assessment of the effectiveness and accuracy of the entire neural network system has been given.