Multimodal Neural Networks in Information Security Systems

Abstract

The article highlights the significant role of introducing multimodal neural networks into information security systems to improve operational efficiency in detecting cyber threats. Using a combination of neural networks, including convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory networks (LSTM), it is possible to achieve high accuracy and speed in detecting cyber threats. By combining multiple data sources such as video surveillance, audio analysis, biometric identification, and behavioural pattern analysis, these multi-modal systems offer comprehensive and in-depth security analysis, making them an effective solution against today’s threats in the information environment. The aim of the study is to analyze and compare the effectiveness of various types of neural networks used in information security, with special attention to the capabilities of multimodal systems. Research objective is to evaluate the use of various types of neural networks in different data processing scenarios, from biometric recognition to network traffic analysis. Research methods are: theoretical analysis and comparison of convolutional neural networks (CNN), recurrent neural networks (RNN) and long short-term memory networks (LSTM). The novelty of the work lies in an integrated approach to analysing multimodal systems in the context of modern cyber threats. Research results: multimodal systems equipped with modern neural networks represent the future in the field of information security. Findings: the analysis confirms the essential role of integrating artificial intelligence into information security systems, emphasizing the importance of multimodal systems in creating effective, adaptive, and scalable solutions for protecting data and information systems in the modern digital environment.