Development of intelligent models for proactive protection of critical infrastructure of the financial sector using the example of information support for contract systems

The paper proposes an approach to developing intelligent models of proactive protection focused on information support of contract systems in the financial sector. A methodology for developing intelligent models is presented, which includes components for monitoring, forecasting and preventing cyberattacks. The proposed methodology formed the basis for practical implementation in Python using the Numpy and Scirket Learn libraries. Particular attention is paid to the use of advanced machine learning and artificial intelligence algorithms to identify and prevent potential threats in real time. As a practical example, the application of the developed intelligent models to protect the information support of contract systems used in the financial sector is considered. Key vulnerabilities, potential attacks and methods for their proactive detection and blocking are analyzed. The results of the study are confirmed by the data of a computational experiment and demonstrate the high efficiency of the proposed approach in increasing the resilience of the critical information infrastructure of the financial sector to cyberattacks. The implementation of intelligent models of proactive protection allows us to significantly reduce the risks of compromising the integrity and availability of key data, minimize financial and reputational losses, and predict and prevent potential threats.

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