Effectiveness of artificial intelligence models to counter cybersecurity threats in IoT devices with Blockchain. A Systematic Review (#997)

Authors

Miranda-Torres, David Alejandro

Sánchez-Goycochea, Nestor Abel

Abstract

Artificial intelligence models have significantly transformed the Internet of Things (IoT) sector, but they have also increased the risks associated with cybersecurity. This research aims to determine which of these models are most effective in countering cybersecurity threats in IoT devices integrated into a Blockchain ecosystem. A systematic literature review was developed, structured around three specific questions derived from the main research question: What are the mosts reliable artificial intelligence models when integrated into a Blockchain ecosystem to counter cybersecurity threats in IoT devices? The analysis was performed using the PRISMA protocol and information sourced from the Scopus and Web of Science databases. The findings reveal that Machine Learning and Deep Learning models stand out in terms of effectiveness, with CNN, LSTM, and Federated Learning identified as the most reliable approaches in IoT environments within Blockchain ecosystems. Accordingly, it is concluded that these techniques provide robust and reliable solutions for mitigating risks in IoT, significantly contributing to the implementation of advanced cybersecurity strategies in organizations and technological sectors.