Methods for developing a deep neural network architecture designed to recognize computer viruses - Терейковський, Ігор Анатолійович; Заріцький, Олег Володимирович; Терейковська, Людмила Олексіївна; Погорелов, Володимир Володимирович (Ukrainian Information Security Research Journal, 2018)
The article is devoted to the solution of the problem of improving computer virus recognition systems. Although the antivirus protection systems have been used for several decades, a lot of highly skilled specialists are involved in their development, and a large number of works are devoted to the creation of the appropriate scientific and methodological base, but practical experience and known cases of successful virus attacks on domestic and foreign computer systems and networks point to the presence in modern antivirus detection of serious shortcomings. It is shown that correcting a number of disadvantages is possible by improving the mathematical support of the recognition procedure due to the use of modern neural network models based on deep neural networks. The method of development of the architecture of the deep neural network intended for the recognition of viruses is proposed. In contrast to the existing method, it is possible to avoid during the development of a neural network model of longterm numerical experiments aimed at determining the appropriateness of its application and optimizing its structural parameters. By numerical experiments using Microsoft's computer virus database BIG-2015 published by Microsoft, it is shown that the method allows constructing a neural network model that provides a recognition error that is commensurate with the error of modern computer virus detection systems. It is determined that the prospects for further research are related to the adaptation of the proposed method to the application of deep neural networks in behavioral analyzers.