Method of adaptation of the deep neural network to the recognition of computer viruses
Abstract
The analysis of scientific and applied researches devoted to creation of the systems of protection against harmful software shows that one of the most perspective directions of development of the systems of recognition of malicious software is the improvement of their mathematical support due to the application of modern neural network models on the basis of deep neural networks. The results of the analysis also identified the need to create a method for developing the architecture of deep neural network adapted to the conditions of use in modern means of recognition. In the course of research, a method for developing a deep neural network architecture designed to detect malicious software was proposed. In contrast to the existing methods, this method allows avoiding during the development of a neural network model of long-term numerical experiments aimed at determining the appropriateness of its application and optimizing its structural parameters. Through numerical experiments using computer virus database BIG-2015 published by Microsoft, it is shown that the method allows building a neural network model that provides a recognition error that is commensurate with the error of modern computer virus recognition systems. Prospects for further research are related to the adaptation of the proposed method to the application of deep neural networks in behavioural analyzers.
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