Authors: Hector Alaiz-Moreton, Jose Aveleira-Mata, Jorge Ondicol-Garcia, Angel Luis Muñoz-Castañeda, Isaías García, Carmen Benavides

Published on: February 05, 2024

Impact Score: 8.38

Arxiv code: Arxiv:2402.0327

Summary

• What is new: The research innovates by applying both ensemble methods and deep learning models, specifically recurrent networks, for classifying attacks in IoT systems.
• Why this is important: IoT networks’ heterogeneity introduces new cybersecurity challenges, necessitating improved intrusion detection systems.
• What the research proposes: Creation of classification models that enhance IDS by utilizing a dataset of frames under attack in an IoT system leveraging the MQTT protocol.
• Results: Both ensemble methods and recurrent networks demonstrated very satisfactory results in classifying attacks.

Technical Details

Technological frameworks used: Not specified

Models used: Ensemble methods, Deep learning models (recurrent networks)

Data used: Dataset containing frames under attacks of an IoT system using MQTT protocol

Potential Impact

Cybersecurity solutions for IoT systems, companies specializing in IoT security, developers of intrusion detection systems

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