A Machine Learning Approach for Prediction of Signaling SIP Dialogs

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/10362/123689
Resumo: POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095 UIDB/EEA/50008/2020
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spelling A Machine Learning Approach for Prediction of Signaling SIP DialogsBayesian networkshidden Markov chainsmachine learningSession initiation protocolComputer Science(all)Materials Science(all)Engineering(all)POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095 UIDB/EEA/50008/2020In this paper, we propose a machine learning methodology for prediction of signaling sessions established with the Session Initiation Protocol (SIP). Given the increasing importance of predicting and detecting abnormal sequences of SIP messages to avoid SIP signaling-based attacks, we first propose a Bayesian inference method capable of representing the statistical relation between a SIP message, observed by a SIP user agent or a SIP server, and prior trustworthy SIP dialogs. The Bayesian inference method, a Hidden Markov Model (HMM) enriched with $n-$ gram Markov observations, is updated over time, so the inference can be used in real-time. The HMM is then used for predicting and detecting SIP dialogs through a lightweight implementation of Viterbi algorithm for sparse state spaces. Experimental results are also reported, where a SIP dataset representing prior information collected by a SIP user agent and/or a SIP server is used to predict or detect if a received sequence of SIP messages is legitimate according to similar SIP dialogs already observed. Finally, we discuss the results obtained for a dataset of abnormal SIP sequences, not observed during the inference stage, showing the effective utility of the proposed methodology to detect abnormal SIP sequences in a short period of time.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNPereira, DiogoOliveira, RodolfoKim, Hyong S.2021-09-03T00:12:38Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/123689engPURE: 29590316https://doi.org/10.1109/ACCESS.2021.3065660info: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:RCAAP2024-03-11T05:05:02Zoai:run.unl.pt:10362/123689Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:45:08.932726Repositório 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 A Machine Learning Approach for Prediction of Signaling SIP Dialogs
title A Machine Learning Approach for Prediction of Signaling SIP Dialogs
spellingShingle A Machine Learning Approach for Prediction of Signaling SIP Dialogs
Pereira, Diogo
Bayesian networks
hidden Markov chains
machine learning
Session initiation protocol
Computer Science(all)
Materials Science(all)
Engineering(all)
title_short A Machine Learning Approach for Prediction of Signaling SIP Dialogs
title_full A Machine Learning Approach for Prediction of Signaling SIP Dialogs
title_fullStr A Machine Learning Approach for Prediction of Signaling SIP Dialogs
title_full_unstemmed A Machine Learning Approach for Prediction of Signaling SIP Dialogs
title_sort A Machine Learning Approach for Prediction of Signaling SIP Dialogs
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 Bayesian networks
hidden Markov chains
machine learning
Session initiation protocol
Computer Science(all)
Materials Science(all)
Engineering(all)
topic Bayesian networks
hidden Markov chains
machine learning
Session initiation protocol
Computer Science(all)
Materials Science(all)
Engineering(all)
description POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095 UIDB/EEA/50008/2020
publishDate 2021
dc.date.none.fl_str_mv 2021-09-03T00:12:38Z
2021
2021-01-01T00:00:00Z
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/10362/123689
url http://hdl.handle.net/10362/123689
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 29590316
https://doi.org/10.1109/ACCESS.2021.3065660
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13
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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