Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/28099
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Figueiredo, J. | - |
dc.contributor.author | Serrão, C. | - |
dc.contributor.author | de Almeida, A. | - |
dc.date.accessioned | 2023-03-01T12:25:45Z | - |
dc.date.available | 2023-03-01T12:25:45Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Figueiredo, J., Serrão, C., & de Almeida, A. (2023). Deep learning model transposition for network intrusion detection systems. Electronics, 12(2), 293. http://dx.doi.org/10.3390/electronics12020293 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | http://hdl.handle.net/10071/28099 | - |
dc.description.abstract | Companies seek to promote a swift digitalization of their business processes and new disruptive features to gain an advantage over their competitors. This often results in a wider attack surface that may be exposed to exploitation from adversaries. As budgets are thin, one of the most popular security solutions CISOs choose to invest in is Network-based Intrusion Detection Systems (NIDS). As anomaly-based NIDS work over a baseline of normal and expected activity, one of the key areas of development is the training of deep learning classification models robust enough so that, given a different network context, the system is still capable of high rate accuracy for intrusion detection. In this study, we propose an anomaly-based NIDS using a deep learning stacked-LSTM model with a novel pre-processing technique that gives it context-free features and outperforms most related works, obtaining over 99% accuracy over the CICIDS2017 dataset. This system can also be applied to different environments without losing its accuracy due to its basis on context-free features. Moreover, using synthetic network attacks, it has been shown that this NIDS approach can detect specific categories of attacks. | eng |
dc.language.iso | eng | - |
dc.publisher | MDPI | - |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04466%2F2020/PT | - |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04466%2F2020/PT | - |
dc.rights | openAccess | - |
dc.subject | Network intrusion detection system (NIDS) | eng |
dc.subject | Intrusion detection | eng |
dc.subject | Anomaly detection | eng |
dc.subject | Deep learning (DL) | eng |
dc.subject | Long short-term memory (LSTM) | eng |
dc.title | Deep learning model transposition for network intrusion detection systems | eng |
dc.type | article | - |
dc.peerreviewed | yes | - |
dc.volume | 12 | - |
dc.number | 2 | - |
dc.date.updated | 2023-03-01T12:24:51Z | - |
dc.description.version | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.doi | 10.3390/electronics12020293 | - |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências Físicas | por |
dc.subject.fos | Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Civil | por |
dc.subject.fos | Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-92865 | - |
iscte.alternateIdentifiers.wos | WOS:000917028700001 | - |
iscte.alternateIdentifiers.scopus | 2-s2.0-85146814100 | - |
iscte.journal | Electronics | - |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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article_92865.pdf | 418,3 kB | Adobe PDF | Ver/Abrir |
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