BLOCKCHAIN-ENABLED SECURITY PROTOCOLS COMBINED WITH AI FOR SECURING NEXT-GENERATION INTERNET OF THINGS (IoT) NETWORKS

Abstract

This study investigates how blockchain-enabled security protocols and artificial intelligence (AI)–based threat analytics jointly influence the perceived security performance of next-generation Internet of Things (IoT) networks. As IoT ecosystems expand across critical sectors, the limitations of traditional security models highlight the need for decentralized trust mechanisms and intelligent, adaptive intrusion detection. Drawing on theories of IoT security requirements, cyber-risk management, and blockchain–AI convergence, the study develops a conceptual framework comprising four constructs: Blockchain-Enabled Security Controls, AI-Driven Threat Analytics, IoT Cyber-Risk Management Maturity, and Contextual Factors, all hypothesized to affect IoT Security Performance. A quantitative, cross-sectional, case-study–based research design was employed, using a structured Likert five-point survey administered to 160 professionals actively engaged in IoT architecture, cybersecurity operations, and system administration. Reliability validation, correlation analysis, and multiple regression modeling were conducted to evaluate the relationships among constructs and to test three hypotheses concerning individual and interactive effects. Descriptive results indicated strong adoption of both blockchain and AI security capabilities, with mean construct scores exceeding the midpoint, and IoT Security Performance achieving the highest mean (4.12). Correlation analysis showed strong positive associations among all variables, especially between AI-based analytics and IoT Security Performance (r = 0.68). Regression results demonstrated that Blockchain-Enabled Security Controls (β = 0.32, p < .001) and AI-Driven Threat Analytics (β = 0.41, p < .001) each exerted significant positive effects on IoT Security Performance, while IoT Cyber-Risk Management Maturity contributed additional explanatory power (β = 0.19, p = .003). Importantly, an interaction term representing the coexistence of high blockchain, and AI capability revealed a positive and statistically significant effect (β = 0.11, p = .033), increasing model explanatory power (ΔR² = 0.03) and confirming that blockchain and AI function synergistically rather than independently. Overall, the findings empirically validate the complementary roles of blockchain and AI in enhancing IoT confidentiality, integrity, availability, and resilience. The study contributes to IoT security scholarship by operationalizing and testing constructs that have largely been addressed conceptually in prior work. It further offers practical insights for organizations seeking integrated, risk-informed security architectures for large-scale IoT environments.