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arXiv · 2602.18598

Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks

María Teresa García-Ordás·Jose Aveleira-Mata·Isaías García-Rodríguez·José Luis Casteleiro-Roca·Martín Bayón-Gutierrez·Héctor Alaiz-Moretón
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Abstract

The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments. Specifically, the study explores the efficacy of an autoencoder's latent space combined with three different classification techniques. Utilizing a validated IoT dataset, particularly focusing on the Constrained Application Protocol (CoAP), the study seeks to develop a robust model capable of identifying security breaches targeting this protocol. The research culminates in a comprehensive evaluation, presenting encouraging results that demonstrate the effectiveness of the proposed methodologies in strengthening IoT cybersecurity with more than a 99% of precision using only 2 learned features.

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María Teresa García-Ordás, Jose Aveleira-Mata, Isaías García-Rodríguez, José Luis Casteleiro-Roca, Martín Bayón-Gutierrez, Héctor Alaiz-Moretón. 2026-02-20. Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks. https://doi.org/10.1093/jigpal%2Fjzae104

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