INTELLIGENT RISK ASSESSMENT MODELS IN DISTRIBUTED SYSTEMS BASED ON THE NEURAL NETWORK APPROACH

Abstract

In modern conditions of information systems functioning, the rapid growth of scale, complexity and distribution of computing resources is becoming one of the defining trends in the development of digital infrastructure. Within the framework of the widespread implementation of complex multi-component information systems that are distributed in nature and contain a large number of nodes, as well as a significant increase in the number and complexity of cyber threats focused on scalable systems, cybersecurity risks should be considered as a key factor in strategic planning of business processes. Regular analysis and assessment of cybersecurity risks allows determining the necessary and sufficient set of information protection tools, regulatory and organizational mechanisms to reduce information security threats, and ensures the process of building the most effective architecture of a comprehensive information security management system. Existing tools and assessment methodologies, which are mostly conceptual in nature and based on statistical approaches, are ineffective in analysis of large arrays of high-dimensional heterogeneous data and metrics of distributed systems. The article focuses on the current trends and existing approaches to information security risk assessment in distributed information systems. It analyzes the importance of risk management in the process of ensuring information security, and also describes a core principles of intelligent security risk assessment in distributed information systems based on the neural network approach. Research also presents a dynamic and comprehensive model of cyber risk assessment in distributed information systems based on back propagation neural network architecture and several methods of its optimization, which provides sufficient accuracy and reliability of risk assessment in the conditions of analysis of large arrays of heterogeneous input data.