The rapid proliferation of Internet of Things (IoT) devices has brought significant advancements across various sectors, yet their widespread use exposes them to numerous cybersecurity risks. This article comprehensively analyses IoT cybersecurity, focusing on common attack vectors, defensive mechanisms, and analytical tools. Key threats such as device spoofing, node capture, and side-channel attacks are detailed alongside effective countermeasures, including encryption, authentication, and secure boot processes. The paper also explores the application of machine learning algorithms, such as Random Forests and Support Vector Machines, in detecting and mitigating IoT-specific threats. Security frameworks, ranging from qualitative approaches like OCTAVE to quantitative methodologies like CVSS, are also evaluated for their relevance in assessing and managing IoT vulnerabilities. This study guides the development of robust IoT security strategies based on recent research.

The rapid proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity challenges due to the heterogeneity and vulnerability of these systems. This paper investigates the integration of Fortinet’s security solutions with IoT systems to enhance threat detection and mitigation capabilities. A hybrid architecture combining conventional networking components with Fortinet technologies, such as FortiGate and FortiAnalyzer, is proposed and implemented. The solution is validated through a series of practical scenarios that simulate real-world network attacks on IoT devices. Results demonstrate improved detection accuracy and response time, emphasizing the effectiveness of Fortinet’s centralized logging and anomaly detection mechanisms. The study provides a scalable framework that can be adapted for various IoT environments, contributing to the development of more secure and resilient network infrastructures.

This paper explores the usability of random graph models from the field of social network analysis for selected topics in the field of economic sciences. Specific examples are given in the field of international trade and strategic research as part of international research analysis. At present, there is an increasing need to connect individuals or organizations to increasingly complex networks, and as the complexity of such structures grows, the need for understanding how such structures are created and what they actually mean. The method of random graphs used in the paper serves as an appropriate method for such analysis.