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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| Format: | Article |
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PeerJ Inc.
2021-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-787.pdf |
| _version_ | 1818681679975284736 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T10:06:47Z |
| format | Article |
| id | doaj.art-282066b9c09d4f4e968417f2d8539d95 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-17T10:06:47Z |
| publishDate | 2021-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-282066b9c09d4f4e968417f2d8539d952022-12-21T21:53:09ZengPeerJ Inc.PeerJ Computer Science2376-59922021-11-017e78710.7717/peerj-cs.787Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architecturesJosé Roldán-Gómez0Juan Boubeta-Puig1Gabriela Pachacama-Castillo2Guadalupe Ortiz3Jose Luis Martínez4Research Institute of Informatics (i3a), Universidad de Castilla La Mancha, Albacete, SpainDepartment of Computer Science and Engineering, University of Cadiz, Cadiz, SpainSchool of Engineering, University of Cadiz, Cadiz, SpainDepartment of Computer Science and Engineering, University of Cadiz, Cadiz, SpainResearch Institute of Informatics (i3a), Universidad de Castilla La Mancha, Albacete, SpainThe 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.https://peerj.com/articles/cs-787.pdfInternet of thingsComplex event processingMachine learningPattern detectionSecurity attack |
| spellingShingle | 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 PeerJ Computer Science Internet of things Complex event processing Machine learning Pattern detection Security attack |
| 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 | Internet of things Complex event processing Machine learning Pattern detection Security attack |
| url | https://peerj.com/articles/cs-787.pdf |
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