Abstract
This article considers tools for controlling software installed on personal computers of automated system users. The flaws of these software solutions are grounded, and an approach to identifying executable files with the help of a machine learning algorithm is developed and presented. This algorithm consists in the gradient decision tree boosting on the basis of such libraries as XGBoost, LightGBM, CatBoost. The identification of programs with the help of XGBoost and LightGBM is executed. The experimental results are compared with the results of earlier studies conducted by other authors. The findings show that the developed method allows for identifying violations in the adopted security policy during information processing in automated systems.
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Funding
This study was conducted under basic research program 7, “New Developments in Cutting-Edge Areas of Power Engineering, Mechanics, and Robotics” of the Russian Academy of Sciences. This program is one of the programs of basic research in focal areas defined by the Presidium of the Russian Academy of Sciences.
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Translated by S. Kuznetsov
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Sukhoparov, M.E., Salakhutdinova, K.I. & Lebedev, I.S. Software Identification by Standard Machine Learning Tools. Aut. Control Comp. Sci. 55, 1175–1179 (2021). https://doi.org/10.3103/S0146411621080459
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DOI: https://doi.org/10.3103/S0146411621080459