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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Autores principales: José Roldán-Gómez, Juan Boubeta-Puig, Gabriela Pachacama-Castillo, Guadalupe Ortiz, Jose Luis Martínez
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Publicado: PeerJ Inc. 2021
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spelling oai:doaj.org-article:282066b9c09d4f4e968417f2d8539d952021-11-25T15:05:22ZDetecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures10.7717/peerj-cs.7872376-5992https://doaj.org/article/282066b9c09d4f4e968417f2d8539d952021-11-01T00:00:00Zhttps://peerj.com/articles/cs-787.pdfhttps://peerj.com/articles/cs-787/https://doaj.org/toc/2376-5992The 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.José Roldán-GómezJuan Boubeta-PuigGabriela Pachacama-CastilloGuadalupe OrtizJose Luis MartínezPeerJ Inc.articleInternet of thingsComplex event processingMachine learningPattern detectionSecurity attackElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e787 (2021)
institution DOAJ
collection DOAJ
language EN
topic Internet of things
Complex event processing
Machine learning
Pattern detection
Security attack
Electronic computers. Computer science
QA75.5-76.95
spellingShingle Internet of things
Complex event processing
Machine learning
Pattern detection
Security attack
Electronic computers. Computer science
QA75.5-76.95
José Roldán-Gómez
Juan Boubeta-Puig
Gabriela Pachacama-Castillo
Guadalupe Ortiz
Jose Luis Martínez
Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures
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.
format article
author José Roldán-Gómez
Juan Boubeta-Puig
Gabriela Pachacama-Castillo
Guadalupe Ortiz
Jose Luis Martínez
author_facet José Roldán-Gómez
Juan Boubeta-Puig
Gabriela Pachacama-Castillo
Guadalupe Ortiz
Jose Luis Martínez
author_sort José Roldán-Gómez
title 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_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_sort detecting security attacks in cyber-physical systems: a comparison of mule and wso2 intelligent iot architectures
publisher PeerJ Inc.
publishDate 2021
url https://doaj.org/article/282066b9c09d4f4e968417f2d8539d95
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