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An intrusion detection system dataset for a multi-agent cyber-physical conveyor system

2023Conference paper Restricted access
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dc.contributor.authorFunchal, Gustavo Silva
dc.contributor.authorZahid, Farzana
dc.contributor.authorMelo, Victoria
dc.contributor.authorKuo, Matthew M.Y.
dc.contributor.authorPedrosa, Tiago
dc.contributor.authorSinha, Roopak
dc.contributor.authorPrieta Pintado, Fernando De la
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2024-01-09T10:12:08Z
dc.date.available2024-01-09T10:12:08Z
dc.date.issued2023
dc.description.abstractIndustry 4.0 is built upon the foundation of connecting devices and systems via Internet of Things (IoT) technologies, with Cyber-Physical Systems (CPS) serving as the backbone infrastructure. Although this approach brings numerous benefits like improved performance, responsiveness and reconfigurability, it also introduces security concerns, making devices and systems vulnerable to cyber attacks. There is a need for effective techniques to protect these systems, and the availability of datasets becomes essential to support the development of such techniques. This paper presents a dataset based on the collection of traffic information exchanged in a self-organizing conveyor system using the multi-agent systems (MAS) architecture and containing various intelligent conveyor modules. The dataset comprises data collected at the network and agent levels under normal system operation, denial of service (DoS) attacks, and malicious agent attacks. An intrusion detection system that integrates Fast Fourier Transform (FFT) and Machine Learning (ML) analysis is developed to demonstrate the utility of this dataset.pt_PT
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The authors Gustavo Funchal and Vict´oria Melo thank the FCT for the PhD Grants 2022.13712.BD and 2022.13868.BD.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFunchal, Gustavo Silva; Zahid, Farzana; Melo, Victoria; Kuo, Matthew M.Y.; Pedrosa, Tiago; Sinha, Roopak; De la Prieta, Fernando; Leitão, Paulo (2023). An intrusion detection system dataset for a multi-agent cyber-physical conveyor system. In 2023 IEEE International Conference on Industrial Technology (ICIT). 04-06 April 2023, Orlando. ISSN 2643-2978. p. 1-6pt_PT
dc.identifier.doi10.1109/ICIT58465.2023.10143037pt_PT
dc.identifier.issn2643-2978
dc.identifier.urihttp://hdl.handle.net/10198/29147
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationLA/P/0007/2021pt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationDeployment of AI-based algorithms along the edge-to-cloud to enhance the cybersecurity in IoT applications
dc.relationDevelopment of a modular, intelligent and distributed Digital Twin architecture towards zero defects manufacturing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCyber-securitypt_PT
dc.subjectCyber-physical systempt_PT
dc.subjectDenial of Servicept_PT
dc.subjectDatasetpt_PT
dc.subjectMachine Learningpt_PT
dc.titleAn intrusion detection system dataset for a multi-agent cyber-physical conveyor systempt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleDeployment of AI-based algorithms along the edge-to-cloud to enhance the cybersecurity in IoT applications
oaire.awardTitleDevelopment of a modular, intelligent and distributed Digital Twin architecture towards zero defects manufacturing
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//2022.13712.BD/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_NORTE/2022.13868.BD/PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2023 IEEE International Conference on Industrial Technology (ICIT)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamPOR_NORTE
person.familyNameFunchal
person.familyNameMelo
person.familyNamePedrosa
person.familyNameLeitão
person.givenNameGustavo Silva
person.givenNameVictória
person.givenNameTiago
person.givenNamePaulo
person.identifierhttps://scholar.google.com/citations?user=eegfgI4AAAAJ&hl=pt-PT&oi=ao
person.identifierhttps://scholar.google.com/citations?user=2JvgMH4AAAAJ&hl=pt-PT
person.identifierA-8390-2011
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person.identifier.ciencia-idB81E-0583-AEDF
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0002-9691-9956
person.identifier.orcid0000-0003-0025-8055
person.identifier.orcid0000-0003-4873-2705
person.identifier.orcid0000-0002-2151-7944
person.identifier.ridG-2249-2011
person.identifier.scopus-author-id57216637887
person.identifier.scopus-author-id57216635456
person.identifier.scopus-author-id35318153700
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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