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Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures

The Internet of Things (IoT) paradigm keeps growing, and many different IoT devices, such as smartphones and smart appliances, are extensively used in smart industries and smart cities. The benefits of this paradigm are obvious, but these IoT environments have brought with them new challenges, such...

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Detalles Bibliográficos
Autores principales: Roldán-Gómez, José, Boubeta-Puig, Juan, Pachacama-Castillo, Gabriela, Ortiz, Guadalupe, Martínez, Jose Luis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627230/
https://www.ncbi.nlm.nih.gov/pubmed/34901434
http://dx.doi.org/10.7717/peerj-cs.787
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author Roldán-Gómez, José
Boubeta-Puig, Juan
Pachacama-Castillo, Gabriela
Ortiz, Guadalupe
Martínez, Jose Luis
author_facet Roldán-Gómez, José
Boubeta-Puig, Juan
Pachacama-Castillo, Gabriela
Ortiz, Guadalupe
Martínez, Jose Luis
author_sort Roldán-Gómez, José
collection PubMed
description The Internet of Things (IoT) paradigm keeps growing, and many different IoT devices, such as smartphones and smart appliances, are extensively used in smart industries and smart cities. The benefits of this paradigm are obvious, but these IoT environments have brought with them new challenges, such as detecting and combating cybersecurity attacks against cyber-physical systems. This paper addresses the real-time detection of security attacks in these IoT systems through the combined used of Machine Learning (ML) techniques and Complex Event Processing (CEP). In this regard, in the past we proposed an intelligent architecture that integrates ML with CEP, and which permits the definition of event patterns for the real-time detection of not only specific IoT security attacks, but also novel attacks that have not previously been defined. Our current concern, and the main objective of this paper, is to ensure that the architecture is not necessarily linked to specific vendor technologies and that it can be implemented with other vendor technologies while maintaining its correct functionality. We also set out to evaluate and compare the performance and benefits of alternative implementations. This is why the proposed architecture has been implemented by using technologies from different vendors: firstly, the Mule Enterprise Service Bus (ESB) together with the Esper CEP engine; and secondly, the WSO2 ESB with the Siddhi CEP engine. Both implementations have been tested in terms of performance and stress, and they are compared and discussed in this paper. The results obtained demonstrate that both implementations are suitable and effective, but also that there are notable differences between them: the Mule-based architecture is faster when the architecture makes use of two message broker topics and compares different types of events, while the WSO2-based one is faster when there is a single topic and one event type, and the system has a heavy workload.
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spelling pubmed-86272302021-12-10 Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures Roldán-Gómez, José Boubeta-Puig, Juan Pachacama-Castillo, Gabriela Ortiz, Guadalupe Martínez, Jose Luis PeerJ Comput Sci Data Mining and Machine Learning The Internet of Things (IoT) paradigm keeps growing, and many different IoT devices, such as smartphones and smart appliances, are extensively used in smart industries and smart cities. The benefits of this paradigm are obvious, but these IoT environments have brought with them new challenges, such as detecting and combating cybersecurity attacks against cyber-physical systems. This paper addresses the real-time detection of security attacks in these IoT systems through the combined used of Machine Learning (ML) techniques and Complex Event Processing (CEP). In this regard, in the past we proposed an intelligent architecture that integrates ML with CEP, and which permits the definition of event patterns for the real-time detection of not only specific IoT security attacks, but also novel attacks that have not previously been defined. Our current concern, and the main objective of this paper, is to ensure that the architecture is not necessarily linked to specific vendor technologies and that it can be implemented with other vendor technologies while maintaining its correct functionality. We also set out to evaluate and compare the performance and benefits of alternative implementations. This is why the proposed architecture has been implemented by using technologies from different vendors: firstly, the Mule Enterprise Service Bus (ESB) together with the Esper CEP engine; and secondly, the WSO2 ESB with the Siddhi CEP engine. Both implementations have been tested in terms of performance and stress, and they are compared and discussed in this paper. The results obtained demonstrate that both implementations are suitable and effective, but also that there are notable differences between them: the Mule-based architecture is faster when the architecture makes use of two message broker topics and compares different types of events, while the WSO2-based one is faster when there is a single topic and one event type, and the system has a heavy workload. PeerJ Inc. 2021-11-23 /pmc/articles/PMC8627230/ /pubmed/34901434 http://dx.doi.org/10.7717/peerj-cs.787 Text en © 2021 Roldán-Gómez et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Roldán-Gómez, José
Boubeta-Puig, Juan
Pachacama-Castillo, Gabriela
Ortiz, Guadalupe
Martínez, Jose Luis
Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures
title Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures
title_full Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures
title_fullStr Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures
title_full_unstemmed Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures
title_short Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures
title_sort detecting security attacks in cyber-physical systems: a comparison of mule and wso2 intelligent iot architectures
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627230/
https://www.ncbi.nlm.nih.gov/pubmed/34901434
http://dx.doi.org/10.7717/peerj-cs.787
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