Visualization Method for Visual Analysis of Security Data Using Neural Networks

The aim of this study is to develop a method for creating effective visualization models that can generate graphs from security data for later visual analysis using artificial intelligence methods. This study systematically analyzes the areas of information security where neural networks are applied, and presents the most common neural network architectures used for image analysis. To develop the method, conceptual visualization models are proposed that take into account the technical features of convolutional neural networks and the differences in perception between human operators and neural networks. The method is based on these conceptual models and a set of visual design principles. This approach will allow one to not only design visualization models suitable for neural network analysis but also automate the work of human operators. The proposed solution can play an important role in developing information security applications. The study also suggests an architecture for a software prototype based on the proposed method. The findings of this study may be useful for developers of decision support systems and researchers working in the field of neural network technology.