Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911461142429696 |
|---|---|
| author | García-Ordás, María Teresa Aveleira-Mata, Jose García-Rodríguez, Isaías Casteleiro-Roca, José Luis Bayón-Gutierrez, Martín Alaiz-Moretón, Héctor |
| author_facet | García-Ordás, María Teresa Aveleira-Mata, Jose García-Rodríguez, Isaías Casteleiro-Roca, José Luis Bayón-Gutierrez, Martín Alaiz-Moretón, Héctor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18598 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks García-Ordás, María Teresa Aveleira-Mata, Jose García-Rodríguez, Isaías Casteleiro-Roca, José Luis Bayón-Gutierrez, Martín Alaiz-Moretón, Héctor Cryptography and Security C.2.0; D.4.6; I.5.2 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. |
| title | Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks |
| topic | Cryptography and Security C.2.0; D.4.6; I.5.2 |
| url | https://arxiv.org/abs/2602.18598 |