Кібербезпека: освіта, наука, техніка (Jun 2022)
ОПТИМІЗАЦІЯ МЕТОДУ ВИБОРУ СТРАТЕГІЇ ІНВЕСТУВАННЯ ЗАСОБІВ ЗАХИСТУ ІНФОРМАЦІЇ НА ОСНОВІ КОМБІНАЦІЇ ТЕОРІЇ ІГОР ТА ГЕНЕТИЧНОГО АЛГОРИТМУ
Abstract
Today, there is a tendency to increase financial income from criminal organizations and increase attacks on information systems. At the same time, new methods and models are being developed to support decision-making regarding the choice of financing strategy. Investments in innovative projects, for example, in the field of information technology and cyber security, in many cases are determined by a high probability of calculation inaccuracy and risk. Data analysis systems are often used to improve the efficiency and optimization of project evaluation procedures and to support investment decision-making. It is decision support systems that make it possible to optimize procedures related to the selection of strategies for financial investment of projects based on a combination of game theory with the help of a genetic algorithm. The lack of standardization of the information field and limited access to structured information regarding the degree of cyber security of a specific informatization object is one of the main problems in the field of information protection and cyber security of many states. The only option for solving the problem of finding a rational strategy for investing in cyber security is only to involve the potential of the decision support system. The article describes a method for a decision-making support system based on a genetic algorithm and a combination of game theory, which contribute to ensuring the continuous and effective functioning of the information resource protection system of the informatization object of any scale. In the developed genetic algorithm, the so-called quality index or degree of achievement of the desired goals for a specific information protection tool is adopted as an integral indicator of information protection tools. The presented method can be applied to reduce the time in solving the problem of finding rational (optimal) strategies of investors based on game models in combination with a genetic algorithm, in particular, in the conditions of dynamic opposition to an attacker, when the evaluation of a rational investment strategy is extremely important for the defense side.
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