Artificial Intelligence for IoT Network Security in Peru: A Systematic Analysis of Threat Detection Strategies

Córdova-Berona H.A.; Delgado Flores J.; Villar Quin S.; Briones-Zuñiga J. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/leird2025.1.1.827

Resumen

This study presents a systematic literature review focused on threat detection strategies in Internet of Things (IoT) networks using artificial intelligence techniques, with a particular emphasis on their applicability to the Peruvian context. The PRISMA protocol was applied for the selection and analysis of studies published between 2020 and 2025, covering a range of topics from decision tree and random forest-based smart irrigation systems to advanced deep learning models (autoencoders, CNNs, LSTMs) and explainable artificial intelligence (XAI) approaches for botnet detection. The review identifies methods that achieve the highest detection rates (> 95%), evaluates their scalability on resource-constrained devices, and examines challenges arising from protocol heterogeneity and the lack of security standards. Furthermore, it describes emerging solutions such as blockchain, physical unclonable functions (PUFs), and federated learning to enhance privacy and resilience against both physical and logical attacks. The findings reveal critical gaps in regional adoption, such as the absence of regulatory frameworks and limited edge computing infrastructure, which hinder the implementation of AI-based detection systems. Based on this analysis, a set of recommendations is proposed to guide the development and integration of robust solutions tailored to the technological, economic, and regulatory characteristics of Peru, fostering a more resilient and reliable IoT cybersecurity architecture. © LEIRD 2025.All rights reserved.

Instituciones: Universidad Tecnológica del Perú