СLASSIFICATIONS OF MACHINE LEARNING APPLICATION MODELS IN CYBER SECURITY
DOI:
https://doi.org/10.32782/tnv-tech.2023.4.2Keywords:
artificial intelligence, machine learning, cyber security, cyber attack, automationAbstract
The article examines the relationship between artificial intelligence (AI) and cybersecurity, analyzing the important challenges and opportunities arising from the rapid development of these two fields. In today’s world, where artificial intelligence is becoming more common and used in various industries, cyber security is becoming one of the most important aspects of information security and protection. The article explains that while AI can bring significant benefits, it also creates new threats and risks to cybersecurity. Overall, the article “Artificial Intelligence and Cybersecurity” offers a comprehensive overview of the relationship between these two fields, focusing on the challenges and opportunities associated with artificial intelligence in the context of cybersecurity. It emphasizes the need to develop effective measures to protect against threats arising from artificial intelligence and emphasizes the continuous improvement of cyber security strategies to ensure the security and protection of information. In the modern interpretation, artificial intelligence systems are machine learning systems, sometimes this is further narrowed down to artificial neural networks. If we are talking about the ever-widening penetration of machine learning into various areas of application of information technologies, then naturally there should be intersections with cyber security. But the problem is that such an intersection cannot be described by any one model. The combination of artificial intelligence and cyber security has many different application aspects. Common is, of course, the use of machine learning methods, but the tasks, and even the results achieved today, are different. For example, if the application of machine learning to detect attacks and intrusions shows real achievements against previously used approaches, then the attacks of the machine learning systems themselves completely defeat possible defenses. This article is devoted to the classification of machine learning application models in cyber security.
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