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Parameterization and performance analysis of a scalable, near real-time packet capturing platform

2024Journal article Open access
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dc.contributor.authorOliveira, Rafael Cardoso de
dc.contributor.authorPedrosa, Tiago
dc.contributor.authorRufino, José
dc.contributor.authorLopes, Rui Pedro
dc.date.accessioned2024-05-23T08:56:15Z
dc.date.available2024-05-23T08:56:15Z
dc.date.issued2024
dc.description.abstractThe rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.pt_PT
dc.description.sponsorshipThis work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project “CybersSeCIP” (NORTE-01-0145-FEDER- 000044). This work was also supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/ 05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationOliveira, Rafael Cardoso de; Pedrosa, Tiago; Rufino, José; Lopes, Rui Pedro (2024). Parameterization and performance analysis of a scalable, near real-time packet capturing platform. Systems. ISSN 2079-8954. 12:4, p. 1-22pt_PT
dc.identifier.doi10.3390/systems12040126pt_PT
dc.identifier.eissn2079-8954
dc.identifier.urihttp://hdl.handle.net/10198/29799
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCybersecuritypt_PT
dc.subjectIDSpt_PT
dc.subjectDistributed systemspt_PT
dc.subjectPacket capturept_PT
dc.titleParameterization and performance analysis of a scalable, near real-time packet capturing platformpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
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/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage22pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleSystemspt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameOliveira
person.familyNamePedrosa
person.familyNameRufino
person.familyNameLopes
person.givenNameRafael Cardoso de
person.givenNameTiago
person.givenNameJosé
person.givenNameRui Pedro
person.identifier.ciencia-idF71B-6628-2D66
person.identifier.ciencia-idB81E-0583-AEDF
person.identifier.ciencia-idC414-F47F-6323
person.identifier.ciencia-id8E14-54E4-4DB5
person.identifier.orcid0000-0003-4997-4757
person.identifier.orcid0000-0003-4873-2705
person.identifier.orcid0000-0002-1344-8264
person.identifier.orcid0000-0002-9170-5078
person.identifier.ridG-2249-2011
person.identifier.scopus-author-id57387127100
person.identifier.scopus-author-id35318153700
person.identifier.scopus-author-id55947199100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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