Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices
Use este enlace para citar
http://hdl.handle.net/2183/36012
A non ser que se indique outra cousa, a licenza do ítem descríbese como Creative Commons CC BY license https://creativecommons.org/licenses/by/4.0/deed.es
Coleccións
- GI-CTC - Artigos [84]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devicesAutor(es)
Data
2024-04Cita bibliográfica
Álvaro Michelena, María Teresa García Ordás, José Aveleira-Mata, David Yeregui Marcos del Blanco, Míriam Timiraos Díaz, Francisco Zayas-Gato, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, Héctor Alaiz-Moretón, José Luis Calvo-Rolle, Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices, Logic Journal of the IGPL, Volume 32, Issue 2, April 2024, Pages 352–365, https://doi.org/10.1093/jigpal/jzae013
Resumo
[Abstract] This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily.
Palabras chave
Beta hebbian learning
t-SNE
ISOMAP
IoT
MQTT
Cyberattack
t-SNE
ISOMAP
IoT
MQTT
Cyberattack
Descrición
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
Versión do editor
Dereitos
Creative Commons CC BY license https://creativecommons.org/licenses/by/4.0/deed.es
ISSN
1367-0751