Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices-Reference-Cited by-同舟云学术

Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices

Published:2024-03-20 Issue:2 Volume:32 Page:352-365
ISSN:1367-0751
Container-title:Logic Journal of the IGPL
language:en
Short-container-title:

Author:

Michelena Álvaro¹,García Ordás María Teresa²,Aveleira-Mata José³,Marcos del Blanco David Yeregui⁴,Timiraos Díaz Míriam⁵,Zayas-Gato Francisco⁶,Jove Esteban⁷,Casteleiro-Roca José-Luis⁸,Quintián Héctor⁹,Alaiz-Moretón Héctor¹⁰,Luis Calvo-Rolle José¹¹

Affiliation:

1. Department of Industrial Engineering , University of A Coruña, CTC, CITIC, 15403 Ferrol, A Coruña, Spain, alvaro.michelena@udc.es

2. Department of Electrical and Systems Engineering , University of León, 24007 León, Spain, mgaro@unileon.es

3. Department of Electrical and Systems Engineering , University of León, 24007 León, Spain, jose.aveleira@unileon.es

4. Department of Mechanic Engineering , Computer and Aerospacial Sciences, University of León, 24007 León, Spain, david.yeregui.marcos@gmail.com

5. Department of Industrial Engineering , University of A Coruña, CTC, CITIC, 15403 Ferrol, A Coruña, Spain, miriam.timiraos.diaz@udc.es

6. Department of Industrial Engineering , University of A Coruña, CTC, CITIC, 15403 Ferrol, A Coruña, Spain, f.zayas.gato@udc.es

7. Department of Industrial Engineering , University of A Coruña, CTC, CITIC, 15403 Ferrol, A Coruña, Spain, esteban.jove@udc.es

8. Department of Industrial Engineering , University of A Coruña, CTC, CITIC, 15403 Ferrol, A Coruña, Spain, jose.luis.casteleiro@udc.es

9. Department of Industrial Engineering , University of A Coruña, CTC, CITIC, 15403 Ferrol, A Coruña, Spain, hector.quintian@udc.es

10. Department of Electrical and Systems Engineering , University of León, 24007 León, Spain, hector.moreton@unileon.es

11. Department of Industrial Engineering , University of A Coruña, CTC, CITIC, 15403 Ferrol, A Coruña, Spain, jlcalvo@udc.es

Abstract

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.

Publisher

Oxford University Press (OUP)

Link

https://academic.oup.com/jigpal/article-pdf/32/2/352/58499078/jzae013.pdf

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