Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks

• URL: http://arxiv.org/abs/2602.18598v1
• Date: Fri, 20 Feb 2026 20:14:02 GMT
• Title: Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks
• Authors: María Teresa García-Ordás, Jose Aveleira-Mata, Isaías García-Rodríguez, José Luis Casteleiro-Roca, Martín Bayón-Gutierrez, Héctor Alaiz-Moretón,
• Abstract summary: The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices.<n>This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments.
• Score: 2.2166578153935785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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