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.
@article{arxiv.2602.18598,
title = {Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks},
author = {María Teresa García-Ordás and Jose Aveleira-Mata and Isaías García-Rodríguez and José Luis Casteleiro-Roca and Martín Bayón-Gutierrez and Héctor Alaiz-Moretón},
journal= {arXiv preprint arXiv:2602.18598},
year = {2026}
}
Comments
16 pages , 2 figures , 1 table. Accepted for publication in Logic Journal of the IGPL