Effectiveness of Artificial Intelligence Models to Counter Cybersecurity Threats in IoT Devices with Blockchain. A Systematic Review

Alejandro Miranda-Torres D.; Abel Sánchez-Goycochea N. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/laccei2025.1.1.997

Resumen

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 most 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 indicate a high level of effectiveness of Machine Learning and Deep Learning-based models in enhancing the security of IoT devices within Blockchain-integrated environments. Notably, Convolutional Neural Networks, Long Short-Term Memory networks, and Federated Learning models demonstrated over 95% effectiveness in early threat detection. Nevertheless, their performance remains contingent upon factors such as dataset variability and computational resource constraints. 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.

Instituciones: Universidad Tecnológica del Perú