Publication:
On the generation of anomaly detection datasets in industrial control systems

Simple item page
dc.contributor.authorCadenas Sarmiento, Cristian
dc.contributor.authorDel Canto Masa, Carlos Javier
dc.contributor.authorMéndez Nistal, Rubén
dc.contributor.authorHuertas Celdrán, Alberto
dc.contributor.authorFernández Maimó, Lorenzo
dc.contributor.authorGarcía Clemente, Félix Jesús
dc.contributor.authorPerales Gómez, Ángel Luis
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.date.accessioned2024-06-28T07:53:52Z
dc.date.available2024-06-28T07:53:52Z
dc.date.copyright© 2019 The authors
dc.date.issued2019-12-06
dc.description.abstractIn recent decades, Industrial Control Systems (ICS) have been affected by heterogeneous cyberattacks that have a huge impact on the physical world and the people's safety. Nowadays, the techniques achieving the best performance in the detection of cyber anomalies are based on Machine Learning and, more recently, Deep Learning. Due to the incipient stage of cybersecurity research in ICS, the availability of datasets enabling the evaluation of anomaly detection techniques is insufficient. In this paper, we propose a methodology to generate reliable anomaly detection datasets in ICS that consists of four steps: attacks selection, attacks deployment, traffic capture and features computation. The proposed methodology has been used to generate the Electra Dataset, whose main goal is the evaluation of cybersecurity techniques in an electric traction substation used in the railway industry. Using the Electra dataset, we train several Machine Learning and Deep Learning models to detect anomalies in ICS and the performed experiments show that the models have high precision and, therefore, demonstrate the suitability of our dataset for use in production systems.es
dc.formatapplication/pdfes
dc.identifier.citationIEE Access, Volume 7, 2019
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2019.2958284
dc.identifier.urihttp://hdl.handle.net/10201/142731
dc.languageenges
dc.publisherIEEE
dc.relationThis work was supported in part by the Spanish Ministry of Science, Innovation and Universities, FEDER funds, under Grant RTI2018-095855-B-I00, and in part by the Government of Ireland, through the IRC Post-Doc Fellowship under Grant GOIPD/2018/466es
dc.relation.requireshttp://hdl.handle.net/10201/197351
dc.relation.requireshttp://hdl.handle.net/10201/197351
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAtribución-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.titleOn the generation of anomaly detection datasets in industrial control systemses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication155e7fdb-63ef-47a4-8068-bc025cfbabb9
relation.isAuthorOfPublicationb8b7f907-8219-40ea-8184-f07cd22cab90
relation.isAuthorOfPublicatione9f00597-7cc7-41a5-b4ff-2c637c810b33
relation.isAuthorOfPublication7bea5da7-b292-45b8-b346-b542de942321
relation.isAuthorOfPublication.latestForDiscovery155e7fdb-63ef-47a4-8068-bc025cfbabb9
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
On_the_Generation_of_Anomaly_Detection_Datasets_in_Industrial_Control_Systems.pdf
Size:
1.9 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.26 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Artículos