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SafeMan: a unified framework to manage cybersecurity and safety in manufacturing industry

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dc.contributor.authorPerales Gómez, Ángel Luis
dc.contributor.authorFernández Maimó, Lorenzo
dc.contributor.authorGarcía Clemente, Félix Jesús
dc.contributor.authorGil Pérez, Manuel
dc.contributor.authorMartínez Pérez, Gregorio
dc.contributor.authorHuertas Celdrán, Alberto
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.date.accessioned2024-06-28T07:57:57Z
dc.date.available2024-06-28T07:57:57Z
dc.date.copyright© 2020 John Wiley & Sons, Ltd.
dc.date.issued2020-08-06
dc.description.abstractIndustrial control systems (ICS) are considered cyber-physical systems that join both cyber and physical worlds. Due to their tight interaction, where humans and robots co-work and co-inhabit in the same workspaces and production lines, cyber-attacks targeting ICS can alter production processes and even bypass safety procedures. As an example, these cyber-attacks could interrupt physical industrial processes and cause potential injuries to workers. In this article, we present SafeMan, a unified management framework based on the Edge Computing paradigm that provides high-performance applications for the detection and mitigation of both cyber-attacks and safety threats in industrial scenarios. Three use cases show specific threats in manufacturing as well as the SafeMan actions carried out to detect and mitigate them. In order to validate our proposal, a pool of experiments was performed with Electra, an industrial dataset with normal network traffic and different cyber-attacks by using a given number of Modbus TCP and S7Comm devices. The experiments measured the runtime performance of anomaly detection techniques based on machine learning and deep learning to detect cyber-attacks in control networks. The experimental results show that Neural Networks report the best performance, being able to examine 217 feature vectors per second over Electra, and therefore demonstrating that it can be used as detection model for SafeMan in real scenarios.es
dc.formatapplication/pdfes
dc.format.extent21
dc.identifier.citationSoftware: Practice and Experience, 2021;51:607–627
dc.identifier.doihttps://doi.org/10.1002/spe.2879
dc.identifier.eissn1097-024X
dc.identifier.issn0038-0644
dc.identifier.urihttp://hdl.handle.net/10201/142752
dc.languageenges
dc.publisherWiley
dc.relationFunding information Government of Ireland, GOIPD/2018/466; Spanish Ministry of Science, Innovation and Universities, RTI2018-095855-B-I00es
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/10.1002/spe.2879
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnomaly detection
dc.subjectCybersecurity
dc.subjectDeep learning
dc.subjectIndustrial control system
dc.subjectMachine learning
dc.subjectSafety
dc.titleSafeMan: a unified framework to manage cybersecurity and safety in manufacturing industryes
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/acceptedVersión
dspace.entity.typePublicationes
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relation.isAuthorOfPublication.latestForDiscoveryb8b7f907-8219-40ea-8184-f07cd22cab90
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