Cybersecurity applications of blockchain and artificial intelligence in the industrial internet of things

Abstract

The adoption of the internet of things (IoT) is a key driver of digitalization in industries worldwide, enabling automation, real-time monitoring, and data-driven decision making. However, as industrial systems become more interconnected, they also become more vulnerable to cyberthreats. This thesis investigates cybersecurity practices within the internet of things (IoT) and industrial internet of things (IIoT). For this purpose, a review of existing literature is conducted. The methodology is based on an evaluation of selected frameworks that incorporate artificial intelligence (AI) and blockchain technologies. The selection was guided by the scope, complexity, and implementation context of each framework to present a comprehensive overview of their applicability in industrial settings. The results reveal how AI facilitates proactive anomaly detection and real-time trust assessment, while blockchain strengthens data security and authentication. The findings demonstrate how these technologies are applied to mitigate potential cybersecurity threats and highlight their effectiveness in enhancing the security and resilience of IoT and industrial IoT systems.