Classification of Abnormal Signaling SIP Dialogs through Deep Learning

Detalhes bibliográficos
Autor(a) principal: Pereira, Diogo
Data de Publicação: 2021
Outros Autores: Oliveira, Rodolfo, Kim, Hyong S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/134496
Resumo: POCI-01-0145-FEDER-030433 UIDB/50008/2020 PRT/BD/152200/2021
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spelling Classification of Abnormal Signaling SIP Dialogs through Deep Learningdeep learningperformance analysisSession initiation protocolvulnerability predictionComputer Science(all)Materials Science(all)Engineering(all)POCI-01-0145-FEDER-030433 UIDB/50008/2020 PRT/BD/152200/2021Due to the high utilization of the Session Initiation Protocol (SIP) in the signaling of cellular networks and voice over IP multimedia systems, the avoidance of security vulnerabilities in SIP systems is a major aspect to assure that the operators can reach satisfactory readiness levels of service. This work is focused on the detection and prediction of abnormal signaling SIP dialogs as they evolve. Abnormal dialogs include two classes: the ones observed so far and thus labeled as abnormal and already known, but also the unknown ones, i.e., specific sequences of SIP messages never observed before. Taking advantage of recent advances in deep learning, we use Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) to detect and predict dialogs already observed. Additionally, and based on the outputs of the LSTM neural network, we propose two different classifiers capable of identifying unknown SIP dialogs, given the high level of vulnerability they may represent for the SIP operation. The proposed approaches achieve higher SIP dialogs detection scores in a shorter time when compared to a reference probabilistic-based approach. Moreover, the proposed detectors of unknown SIP dialogs achieve a detection probability above 94%, indicating its capability to detect a significant number of unknown SIP dialogs in a short amount of time.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNPereira, DiogoOliveira, RodolfoKim, Hyong S.2022-03-14T23:23:23Z2021-12-132021-12-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://hdl.handle.net/10362/134496engPURE: 42113811https://doi.org/10.1109/ACCESS.2021.3135195info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:12:54Zoai:run.unl.pt:10362/134496Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:08.346342Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Classification of Abnormal Signaling SIP Dialogs through Deep Learning
title Classification of Abnormal Signaling SIP Dialogs through Deep Learning
spellingShingle Classification of Abnormal Signaling SIP Dialogs through Deep Learning
Pereira, Diogo
deep learning
performance analysis
Session initiation protocol
vulnerability prediction
Computer Science(all)
Materials Science(all)
Engineering(all)
title_short Classification of Abnormal Signaling SIP Dialogs through Deep Learning
title_full Classification of Abnormal Signaling SIP Dialogs through Deep Learning
title_fullStr Classification of Abnormal Signaling SIP Dialogs through Deep Learning
title_full_unstemmed Classification of Abnormal Signaling SIP Dialogs through Deep Learning
title_sort Classification of Abnormal Signaling SIP Dialogs through Deep Learning
author Pereira, Diogo
author_facet Pereira, Diogo
Oliveira, Rodolfo
Kim, Hyong S.
author_role author
author2 Oliveira, Rodolfo
Kim, Hyong S.
author2_role author
author
dc.contributor.none.fl_str_mv DEE - Departamento de Engenharia Electrotécnica e de Computadores
RUN
dc.contributor.author.fl_str_mv Pereira, Diogo
Oliveira, Rodolfo
Kim, Hyong S.
dc.subject.por.fl_str_mv deep learning
performance analysis
Session initiation protocol
vulnerability prediction
Computer Science(all)
Materials Science(all)
Engineering(all)
topic deep learning
performance analysis
Session initiation protocol
vulnerability prediction
Computer Science(all)
Materials Science(all)
Engineering(all)
description POCI-01-0145-FEDER-030433 UIDB/50008/2020 PRT/BD/152200/2021
publishDate 2021
dc.date.none.fl_str_mv 2021-12-13
2021-12-13T00:00:00Z
2022-03-14T23:23:23Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/134496
url http://hdl.handle.net/10362/134496
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 42113811
https://doi.org/10.1109/ACCESS.2021.3135195
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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