Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison

Detalhes bibliográficos
Autor(a) principal: Pereira, Diogo
Data de Publicação: 2022
Outros Autores: Oliveira, Rodolfo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/134195
Resumo: UIDB/ 50008/2020
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spelling Detection of Abnormal SIP Signaling Patterns: A Deep Learning ComparisonDeep learningMultimedia networksSIP protocolHuman-Computer InteractionComputer Networks and CommunicationsUIDB/ 50008/2020This paper investigates the detection of abnormal sequences of signaling packets purposely generated to perpetuate signaling-based attacks in computer networks. The problem is studied for the Session Initiation Protocol (SIP) using a dataset of signaling packets exchanged by multiple end-users. A sequence of SIP messages never observed before can indicate possible exploitation of a vulnerability and its detection or prediction is of high importance to avoid security attacks due to unknown abnormal SIP dialogs. The paper starts to briefly characterize the adopted dataset and introduces multiple definitions to detail how the deep learning-based approach is adopted to detect possible attacks. The proposed solution is based on a convolutional neural network capable of exploring the definition of an orthogonal space representing the SIP dialogs. The space is then used to train the neural network model to classify the type of SIP dialog according to a sequence of SIP packets prior observed. The classifier of unknown SIP dialogs relies on the statistical properties of the supervised learning of known SIP dialogs. Experimental results are presented to assess the solution in terms of SIP dialogs prediction, unknown SIP dialogs detection, and computational performance, demonstrating the usefulness of the proposed methodology to rapidly detect signaling-based attacks.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNPereira, DiogoOliveira, Rodolfo2022-03-09T23:25:09Z2022-02-172022-02-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/134195engPURE: 42113743https://doi.org/10.3390/computers11020027info: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:RCAAP2024-03-11T05:12:38Zoai:run.unl.pt:10362/134195Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:00.856306Repositório 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 Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
title Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
spellingShingle Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
Pereira, Diogo
Deep learning
Multimedia networks
SIP protocol
Human-Computer Interaction
Computer Networks and Communications
title_short Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
title_full Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
title_fullStr Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
title_full_unstemmed Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
title_sort Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison
author Pereira, Diogo
author_facet Pereira, Diogo
Oliveira, Rodolfo
author_role author
author2 Oliveira, Rodolfo
author2_role 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
dc.subject.por.fl_str_mv Deep learning
Multimedia networks
SIP protocol
Human-Computer Interaction
Computer Networks and Communications
topic Deep learning
Multimedia networks
SIP protocol
Human-Computer Interaction
Computer Networks and Communications
description UIDB/ 50008/2020
publishDate 2022
dc.date.none.fl_str_mv 2022-03-09T23:25:09Z
2022-02-17
2022-02-17T00:00:00Z
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/10362/134195
url http://hdl.handle.net/10362/134195
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 42113743
https://doi.org/10.3390/computers11020027
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instacron:RCAAP
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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
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