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A scalable, real-time packet capturing solution

2021Conference object Restricted access
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dc.contributor.authorOliveira, Rafael Cardoso de
dc.contributor.authorAlmeida, João P.
dc.contributor.authorPraça, Isabel
dc.contributor.authorLopes, Rui Pedro
dc.contributor.authorPedrosa, Tiago
dc.date.accessioned2022-04-05T08:31:51Z
dc.date.available2022-04-05T08:31:51Z
dc.date.issued2021
dc.description.abstractThe evolution of technology and the increasing connectivity between devices lead to an increased risk of cyberattacks. Good protection systems, such as Intrusion Detection System (IDS) and Intrusion Prevention System (IPS), are essential in trying to prevent, detect and counter most of the attacks. However, the increasing creativity and type of attacks raise the need for more resources and processing power for the protection systems which, in turn, requires horizontal scalability to keep up with the massive companies’ network infrastructure and with the complexity of attacks. Technologies like machine learning, show promising results and can be of added value in the detection and prevention of attacks in real-time. But good algorithms and tools are not enough. They require reliable and solid datasets to be able to effectively train the protection systems. The development of a good dataset requires horizontalscalable, robust, modular and fault-tolerance systems, so that the analyses may be done also in real-time. This paper describes an architecture for horizontal-scaling capture architecture, able to collect packets from multiple sources and prepared for real-time analysis. It depends on multiple modular nodes with specific roles to support different algorithms and tools.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).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationOliveira, Rafael Cardoso de; Almeida, João P.; Praça, Isabel; Lopes, Rui Pedro; Pedrosa, Tiago (2021). A scalable, real-time packet capturing solution. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 630-637. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_46pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25331
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPacket capturept_PT
dc.subjectPacket storagept_PT
dc.subjectDistributed systempt_PT
dc.subjectMachine learningpt_PT
dc.titleA scalable, real-time packet capturing solutionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage637pt_PT
oaire.citation.startPage630pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
person.familyNameOliveira
person.familyNameAlmeida
person.familyNameLopes
person.familyNamePedrosa
person.givenNameRafael Cardoso de
person.givenNameJoão P.
person.givenNameRui Pedro
person.givenNameTiago
person.identifierR-000-K6T
person.identifier.ciencia-idF71B-6628-2D66
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person.identifier.ciencia-id8E14-54E4-4DB5
person.identifier.ciencia-idB81E-0583-AEDF
person.identifier.orcid0000-0003-4997-4757
person.identifier.orcid0000-0002-1286-2527
person.identifier.orcid0000-0002-9170-5078
person.identifier.orcid0000-0003-4873-2705
person.identifier.ridN-8243-2013
person.identifier.ridG-2249-2011
person.identifier.scopus-author-id57387127100
person.identifier.scopus-author-id54956738400
person.identifier.scopus-author-id35318153700
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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