Ai-based optimization of blockchain protocols for cybersecurity in iot systems in medium-sized organizations

The Internet of Things (IoT) has enabled small and medium enterprises (SMEs) to automate and make their operations more efficient. Unfortunately, the need to keep these facilities online exposes them to cybersecurity threats that can wreak havoc on their reliability and integrity. The object of this study is to fill this gap by assessing AI-driven optimizations and their effects on real-time threat detection and response. Data regarding enterprise IoT utilization and customers' behavior were collected and analyzed using statistical methods such as percentage distributions, normality tests, linear regression, analysis of variance (ANOVA), etc. The improved outcomes in cybersecurity of AI-enhanced blockchain protocols were validated using the Amazon IoT dataset, yielding a relative coefficient R² of 0.947. AI also played a sig. positive role in real-time threat detection and mitigation, backed by p-values (<0.05) and statistical sig. (>2). These results place emphasis on the continuous training of AI models and regular updates of blockchain protocols as mandatory for the sustainability of the system, resilience, and ability to withstand multi-pronged and persistent threats.