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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15164710 |
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Table of Contents:
- <p><span>The widespread presence of Internet of Things devices has transformed diverse industries, empowering data-driven decision-making and automation. However, the massive volume of data generated by these devices also presents significant challenges, particularly in identifying anomalous behaviors that may indicate system malfunctions, security breaches, or other critical issues. This research paper investigates the application of machine learning techniques for anomaly detection within IoT ecosystems, exploring various algorithms, methodologies, and their effectiveness in addressing the unique characteristics of IoT data. Additionally, the paper aims to examine the challenges and limitations associated with these techniques.</span></p>