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Intelligent and dynamic ransomware spread detection and mitigation in integrated clinical environments

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dc.contributor.authorFernández Maimó, Lorenzo
dc.contributor.authorPerales Gómez, Ángel Luis
dc.contributor.authorGarcía Clemente, Félix J.
dc.contributor.authorWeimer, James
dc.contributor.authorLee, Insup
dc.contributor.authorHuertas Celdrán, Alberto
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.date.accessioned2024-06-28T07:52:06Z
dc.date.available2024-06-28T07:52:06Z
dc.date.issued2019-03-05
dc.description© 2019. The authors. This document is made available under the CC-BY-SA 4.0 license http://creativecommons.org/licenses/by-sa/4.0/ This document is the published version of a published work that appeared in final form in IEEE Access. To access the final work, see DOI: https://doi.org/10.3390/s19051114
dc.description.abstractMedical Cyber-Physical Systems (MCPS) hold the promise of reducing human errors and optimizing healthcare by delivering new ways to monitor, diagnose and treat patients through integrated clinical environments (ICE). Despite the benefits provided by MCPS, many of the ICE medical devices have not been designed to satisfy cybersecurity requirements and, consequently, are vulnerable to recent attacks. Nowadays, ransomware attacks account for 85% of all malware in healthcare, and more than 70% of attacks confirmed data disclosure. With the goal of improving this situation, the main contribution of this paper is an automatic, intelligent and real-time system to detect, classify, and mitigate ransomware in ICE. The proposed solution is fully integrated with the ICE++ architecture, our previous work, and makes use of Machine Learning (ML) techniques to detect and classify the spreading phase of ransomware attacks affecting ICE. Additionally, Network Function Virtualization (NFV) and Software Defined Networking (SDN)paradigms are considered to mitigate the ransomware spreading by isolating and replacing infected devices. Different experiments returned a precision/recall of 92.32%/99.97% in anomaly detection, an accuracy of 99.99% in ransomware classification, and promising detection and mitigation times. Finally, different labelled ransomware datasets in ICE have been created and made publicly available.es
dc.formatapplication/pdfes
dc.identifier.citationSensors 2019, 19(5), 1114
dc.identifier.doihttps://doi.org/10.3390/s19051114
dc.identifier.urihttp://hdl.handle.net/10201/142729
dc.languageenges
dc.publisherMDPI
dc.relationThis work has been supported by the Irish Research Council, under the government of Ireland post-doc fellowship (grant GOIPD/2018/466) and a Séneca Foundation grant within the Human Resources Researching Postdoctoral Program 2017.es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/19/5/1114
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAtribución-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.titleIntelligent and dynamic ransomware spread detection and mitigation in integrated clinical environmentses
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
relation.isAuthorOfPublication155e7fdb-63ef-47a4-8068-bc025cfbabb9
relation.isAuthorOfPublication.latestForDiscovery155e7fdb-63ef-47a4-8068-bc025cfbabb9
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