Improving cybersecurity with artificial intelligence
Abstract
The study aimed to explore the possibilities of using artificial intelligence to improve cybersecurity systems in the context of increasing complexity and frequency of cyber threats. Analysis of the effectiveness of integrating machine and deep learning methods into the processes of detecting, assessing and neutralisation threats, as well as identification of the strengths and weaknesses of such approaches, were emphasised. The study substantiated the need to combine the technological capabilities of artificial intelligence with expert human experience to ensure comprehensive and adaptive protection of information systems. The study examined the potential application of artificial intelligence to improve cybersecurity systems, given the growing threats and complexity of modern cybercrime. The impact of machine learning and deep learning technologies on improving the effectiveness of traditional methods of protecting information systems was analysed. The study noted that despite the unchanged basic motivations of cybercriminals, their methods were becoming more sophisticated, which required new approaches to detecting and neutralising threats. The use of artificial intelligence was defined as one of the most promising areas of cybersecurity development, as it allows for the automation of risk assessment and incident response processes, reducing response times and increasing overall security efficiency. Particular attention was devoted to an analysis of the strengths and weaknesses of artificial intelligence in the context of cybersecurity. The necessity of integrating artificial intelligence with human intuition and experience was substantiated, as the combination of these components proved to be the most effective approach to ensuring comprehensive security. In addition, the potential risks and concerns associated with the use of artificial intelligence in cybersecurity were explored. The study concluded that a holistic approach that considers both technical and social aspects is needed to increase the maturity of cybersecurity systems. The importance of socially responsible use of artificial intelligence was emphasised to minimise potential threats and ensure the resilience of cyber systems to new challenges. The practical value of the study is to develop recommendations for the introduction of artificial intelligence into existing cybersecurity systems, which allows them to increase their resilience to new and complex cyber threats, as well as to identify potential risks and shortcomings in existing approaches to information security
Keywords
machine learning; neural networks; network traffic analysis; threat detection; automatic recognition; adaptive response
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