CONCEPTUAL MODEL OF ORGANIZATIONAL AND TECHNICAL SYSTEM FOR CYBER SECURITY OF IoT PLATFORM

Abstract

The article presents a comprehensive exploration of how the development of Internet of Speech technologies, a subset of the Internet of Things (IoT), contributes to improved security measures through the application of differential confidentiality methods. The central argument of the study is that enhancing security in intelligent systems requires innovative approaches to data protection, especially in environments characterized by constant data exchange and user interaction. The authors propose the integration of differential confidentiality to mitigate risks related to unauthorized access to critical digital resources such as storage units, software platforms, databases, and archival systems. This technique ensures that sensitive information remains protected even when shared or processed, by introducing controlled statistical noise that obscures personal identifiers. A key contribution of the article is the justification of a nominal-structural method as a foundational element of an individualized security model. This method is designed to manage and monitor the interplay between internal and external components of an intelligent system, thereby maintaining system coherence and strengthening resistance to cyber threats. The study outlines how this structural framework facilitates efficient connection management and adaptability to various system states and operational conditions. Furthermore, the paper provides a thorough analysis of both organizational and technical security measures within the IoT ecosystem. These include life cycle management of IoT devices, the development of robust security architectures, the importance of continuous personnel training, implementation of encryption standards, real-time system monitoring, and configuration management.

Additionally, the article explores the mathematical underpinnings of differential confidentiality, offering formal models that support its practical implementation. The authors emphasize the dual impact of this method: while it intentionally adds noise to data for anonymization purposes, it paradoxically strengthens overall system security by preventing precise data extraction, thereby enhancing the integrity and resilience of robotic and intelligent systems.