Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture

New applications of industrial automation request great flexibility in the systems, supported by the increase in the interconnection between its components, allowing access to all the information of the system and its reconfiguration based on the changes that occur during its operations, with the pu...

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Autores:
Paredes Valencia, Carlos Mario
Martínez Castro, Diego
Ibarra-Junquera, Vrani
González Potes, Apolinar
Tipo de recurso:
Article of investigation
Fecha de publicación:
2021
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/13869
Acceso en línea:
https://hdl.handle.net/10614/13869
https://red.uao.edu.co/
Palabra clave:
Detección de anomalías (Seguridad informática)
Redes neuronales (Computadores)
Anomaly detection (Computer security)
Neural networks (Computer science)
Anomaly detection
Anomaly isolation
Artificial neural networks
Cyber Physical System
Rights
openAccess
License
Derechos reservados - MDPI, 2021
id REPOUAO2_49ec1f020fcd86d1cffff7744691c78c
oai_identifier_str oai:red.uao.edu.co:10614/13869
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
title Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
spellingShingle Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
Detección de anomalías (Seguridad informática)
Redes neuronales (Computadores)
Anomaly detection (Computer security)
Neural networks (Computer science)
Anomaly detection
Anomaly isolation
Artificial neural networks
Cyber Physical System
title_short Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
title_full Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
title_fullStr Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
title_full_unstemmed Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
title_sort Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architecture
dc.creator.fl_str_mv Paredes Valencia, Carlos Mario
Martínez Castro, Diego
Ibarra-Junquera, Vrani
González Potes, Apolinar
dc.contributor.author.none.fl_str_mv Paredes Valencia, Carlos Mario
Martínez Castro, Diego
Ibarra-Junquera, Vrani
González Potes, Apolinar
dc.subject.armarc.spa.fl_str_mv Detección de anomalías (Seguridad informática)
Redes neuronales (Computadores)
topic Detección de anomalías (Seguridad informática)
Redes neuronales (Computadores)
Anomaly detection (Computer security)
Neural networks (Computer science)
Anomaly detection
Anomaly isolation
Artificial neural networks
Cyber Physical System
dc.subject.armarc.eng.fl_str_mv Anomaly detection (Computer security)
Neural networks (Computer science)
dc.subject.proposal.eng.fl_str_mv Anomaly detection
Anomaly isolation
Artificial neural networks
Cyber Physical System
description New applications of industrial automation request great flexibility in the systems, supported by the increase in the interconnection between its components, allowing access to all the information of the system and its reconfiguration based on the changes that occur during its operations, with the purpose of reaching optimum points of operation. These aspects promote the Smart Factory paradigm, integrating physical and digital systems to create smarts products and processes capable of transforming conventional value chains, forming the Cyber-Physical Systems (CPSs). This flexibility opens a large gap that affects the security of control systems since the new communication links can be used by people to generate attacks that produce risk in these applications. This is a recent problem in the control systems, which originally were centralized and later were implemented as interconnected systems through isolated networks. To protect these systems, strategies that have presented acceptable results in other environments, such as office environments, have been chosen. However, the characteristics of these applications are not the same, and the results achieved are not as expected. This problem has motivated several efforts in order to contribute from different approaches to increase the security of control systems. Based on the above, this work proposes an architecture based on artificial neural networks for detection and isolation of cyber attacks Denial of Service (DoS) and integrity in CPS. Simulation results of two test benches, the Secure Water Treatment (SWaT) dataset, and a tanks system, show the effectiveness of the proposal. Regarding the SWaT dataset, the scores obtained from the recall and F1 score metrics was 0.95 and was higher than other reported works, while, in terms of precision and accuracy, it obtained a score of 0.95 which is close to other proposed methods. With respect to the interconnected tank system, scores of 0.96, 0.83, 0.81, and 0.83 were obtained for the accuracy, precision, F1 score, and recall metrics, respectively. The high true negatives rate in both cases is noteworthy. In general terms, the proposal has a high effectiveness in detecting and locating the proposed attacks
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-09-12
dc.date.accessioned.none.fl_str_mv 2022-05-16T13:29:18Z
dc.date.available.none.fl_str_mv 2022-05-16T13:29:18Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.eng.fl_str_mv Text
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.eng.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.issn.spa.fl_str_mv 20799292
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/13869
dc.identifier.doi.none.fl_str_mv 10.3390/electronics10182238
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Repositorio Educativo Digital
dc.identifier.repourl.spa.fl_str_mv https://red.uao.edu.co/
identifier_str_mv 20799292
10.3390/electronics10182238
Universidad Autónoma de Occidente
Repositorio Educativo Digital
url https://hdl.handle.net/10614/13869
https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 28
dc.relation.citationissue.spa.fl_str_mv 18
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 10
dc.relation.cites.eng.fl_str_mv Paredes Valencia, C. M., Martínez Castro, D., Ibarra Junquera, V., González Potes, A. (2021). Detection and isolation of DoS and integrity cyber attacks in Cyber-Physical Systems with a Neural network-based architecture. Electronics. Vol 10 (18), pp. 1-28. https://www.mdpi.com/2079-9292/10/18/2238/htm
dc.relation.ispartofjournal.eng.fl_str_mv Electronics
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spelling Paredes Valencia, Carlos Mario8257120625861258cc80872af33e0e4fMartínez Castro, Diegovirtual::2997-1Ibarra-Junquera, Vrani775c0fcd76dad13f53399833b2132d2aGonzález Potes, Apolinarc311f3dc09d0b372b60e203f2e5e81fc2022-05-16T13:29:18Z2022-05-16T13:29:18Z2021-09-1220799292https://hdl.handle.net/10614/1386910.3390/electronics10182238Universidad Autónoma de OccidenteRepositorio Educativo Digitalhttps://red.uao.edu.co/New applications of industrial automation request great flexibility in the systems, supported by the increase in the interconnection between its components, allowing access to all the information of the system and its reconfiguration based on the changes that occur during its operations, with the purpose of reaching optimum points of operation. These aspects promote the Smart Factory paradigm, integrating physical and digital systems to create smarts products and processes capable of transforming conventional value chains, forming the Cyber-Physical Systems (CPSs). This flexibility opens a large gap that affects the security of control systems since the new communication links can be used by people to generate attacks that produce risk in these applications. This is a recent problem in the control systems, which originally were centralized and later were implemented as interconnected systems through isolated networks. To protect these systems, strategies that have presented acceptable results in other environments, such as office environments, have been chosen. However, the characteristics of these applications are not the same, and the results achieved are not as expected. This problem has motivated several efforts in order to contribute from different approaches to increase the security of control systems. Based on the above, this work proposes an architecture based on artificial neural networks for detection and isolation of cyber attacks Denial of Service (DoS) and integrity in CPS. Simulation results of two test benches, the Secure Water Treatment (SWaT) dataset, and a tanks system, show the effectiveness of the proposal. Regarding the SWaT dataset, the scores obtained from the recall and F1 score metrics was 0.95 and was higher than other reported works, while, in terms of precision and accuracy, it obtained a score of 0.95 which is close to other proposed methods. With respect to the interconnected tank system, scores of 0.96, 0.83, 0.81, and 0.83 were obtained for the accuracy, precision, F1 score, and recall metrics, respectively. The high true negatives rate in both cases is noteworthy. In general terms, the proposal has a high effectiveness in detecting and locating the proposed attacks28 páginasapplication/pdfengMDPIDerechos reservados - MDPI, 2021https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Detection and isolation of dos and integrity cyber attacks in cyber-physical systems with a neural network-based architectureArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Detección de anomalías (Seguridad informática)Redes neuronales (Computadores)Anomaly detection (Computer security)Neural networks (Computer science)Anomaly detectionAnomaly isolationArtificial neural networksCyber Physical System2818110Paredes Valencia, C. 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[CrossRef]Comunidad en generalPublication16469e35-6f18-4e0c-acfe-e8a2e314fedfvirtual::2997-116469e35-6f18-4e0c-acfe-e8a2e314fedfvirtual::2997-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000195928virtual::2997-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/705671fc-3339-41ff-8d57-296906fe0982/download20b5ba22b1117f71589c7318baa2c560MD52ORIGINALDetection and isolation of DoS and integrity cyber attacks in Cyber-Physical Systems with a Neural network-based architecture.pdfDetection and isolation of DoS and integrity cyber attacks in Cyber-Physical Systems with a Neural network-based architecture.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf614418https://red.uao.edu.co/bitstreams/617d2f67-bb9c-41e4-8c8d-770daf7d2e5d/download5451b4d74f2d88293f790ac601b24784MD53TEXTDetection and isolation of DoS and integrity cyber attacks in Cyber-Physical Systems with a Neural network-based architecture.pdf.txtDetection and isolation of DoS and integrity cyber attacks in Cyber-Physical Systems with a Neural network-based architecture.pdf.txtExtracted texttext/plain88251https://red.uao.edu.co/bitstreams/8f2285e1-9e11-4641-98f7-c572b15d56e4/downloadd104a218ba56f72cd0670b47b6030ec5MD54THUMBNAILDetection and isolation of DoS and integrity cyber attacks in Cyber-Physical Systems with a Neural network-based architecture.pdf.jpgDetection and isolation of DoS and integrity cyber attacks in Cyber-Physical Systems with a Neural network-based architecture.pdf.jpgGenerated Thumbnailimage/jpeg15901https://red.uao.edu.co/bitstreams/2528a1cb-b4c7-4028-bdae-0429bedc712a/download9296fd60c0701c7b186bd9672f04a144MD5510614/13869oai:red.uao.edu.co:10614/138692024-03-07 16:47:19.956https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - MDPI, 2021open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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