METHODS OF NATURAL LANGUAGE ANALYSIS USING NEURAL NETWORKS IN CYBER SECURITY
DOI:
https://doi.org/10.28925/2663-4023.2024.24.398414Keywords:
Natural Language Processing; deep learning; transformers; machine translation; emotion analysis; neural networks; interpretability; multilingualism; ethical aspects; pre-trained models; cybersecurity; information security.Abstract
The work emphasizes the relevance of natural language processing (NLP) in the modern world, in particular due to the constant growth of text data in social networks, e-commerce and online media. The authors note that the effective processing of such data is critically important for business and public administration, as it allows generating new knowledge, predicting trends and making informed decisions. NLP also makes a significant contribution to improving the efficiency of organizations by automating the processing of text information (for example, in customer support systems and feedback analysis). In addition, the article emphasizes the significant prospects for the application of NLP in the field of cybersecurity. In particular, NLP is used for automatic anomaly detection, network traffic monitoring and detection of phishing attacks. For such tasks, deep models (for example, RNN, LSTM, CNN) are used, as well as the latest transformer architectures that are capable of processing large amounts of information in real time. The work also raises important questions related to the challenges of modern NLP, including the need for large computational resources, multilingualism, model interpretation issues, and ethical aspects such as bias and privacy. Finally, the authors note the prospects for the development of NLP, including the study of more efficient algorithms to reduce the resource consumption of models, the creation of more interpretable models that can explain their decisions, as well as the development of methods to support low-resource languages, which will help expand the use of NLP technologies on a global scale. NLP is one of the most dynamic and important branches of artificial intelligence, which allows computers to understand, interpret, and generate human language. In this article, we conduct a detailed review of modern methods and technologies in the field of NLP, analyzing the latest scientific articles and research. We consider the development of technologies, their relevance and novelty, and also deeply analyze the problems and shortcomings of existing approaches. In addition, we compare the effectiveness of different methods and provide recommendations for future research.
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