ANTIVIRUS SYSTEM BASED ON ARTIFICIAL INTELLIGENCE CROSS¬LEARNING TECHNOLOGY

Abstract

The work is devoted to the priority task in the field of digital transformation, namely, ensuring the protection of information systems and technologies. Artificial intelligence has become one of the key tools in solving this problem. The paper considers an anti-virus system based on artificial intelligence crosstraining technology. It presents statistical data confirming the need to implement the technology described in the paper due to a sharp increase in the number of infections of information systems and cyber attacks in recent years. The purpose of this development is to increase the efficiency and reliability of anti-virus systems, providing adaptability to the appearance of new types of cyberthreats. Based on a review of existing analogues for implementing artificial intelligence in cybersecurity, the main drawbacks of using this technology were identified, which were taken into account when developing an antivirus system based on artificial intelligence cross-training technology. The working principle of the proposed solution and the expected benefits from its use are formulated. Two main components of the system – the operation algorithm and the database – are considered in detail. The structural scheme of interaction between the elements in the cross-learning method is presented. The constituent elements of this scheme are described and the purpose of each of them is dissected. The structures of the proposed neural networks are considered. The fundamental logic of the used algorithms and types of mathematical methods is given. All databases necessary to provide the functionality of the described learning algorithm, and their purpose are disassembled. The choice of tools to implement cross-learning technology is justified. In conclusion, a conclusion is made about the possibilities of application of the antivirus system described in the work and about the main difficulties of its implementation.