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Correlation-Based Abnormal SIP Dialog Identification

2025Artigo científico Acesso aberto
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dc.contributor.authorFeio, Clarisse
dc.contributor.authorPereira, Diogo
dc.contributor.authorOliveira, Rodolfo
dc.contributor.authorAmaral, Pedro
dc.contributor.institutionDEE - Departamento de Engenharia Electrotécnica e de Computadores
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2025-09-25T22:08:48Z
dc.date.available2025-09-25T22:08:48Z
dc.date.issued2025
dc.descriptionPublisher Copyright: © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
dc.description.abstractThe Session Initiation Protocol (SIP) is crucial in establishing, maintaining, and terminating multimedia sessions. It is particularly vital for the operation of 4G/5G networks, where the network’s low latency and high reliability enable advanced services such as real-time video streaming and Internet of Things applications. The widespread use of SIP in various network generations emphasizes the need for robust security mechanisms to protect against potential vulnerabilities. SIP is susceptible to various attacks, including registration hijacking, call tampering, and denial of service. A specific threat arises from exploiting unknown or abnormal SIP dialogs to uncover weaknesses in the different SIP implementations running on servers. In this paper, we propose an innovative methodology for anomalous SIP dialog detection based on prior knowledge of observed correct and anomalous SIP dialogs. The proposed approach leverages cross-correlation techniques to score the similarity of the SIP dialogs and the use of statistical metrics to classify the anomalous ones. Our method achieves an accuracy of approximately 98.91%. We compare its performance with the optimal Bayesian solution, a deep learning-based approach, and a hybrid method using both deep-learning and statistical methods. While our solution is close to the optimal accuracy, it does not achieve the lowest false alarm rate. However, it offers a significant advantage in computational efficiency, being over 1000 times faster than both the optimal Bayesian and deep learning methods. These findings underscore the potential of the proposed technique for real-time detection of abnormal SIP dialogs in high-performance network environments. Cross-correlation was also employed to predict the SIP ID of ongoing SIP dialogs before their full arrival. Although this method was faster than the other studied methods, its predictive performance was suboptimal, achieving high accuracy only when over 90% of the data was available. Based on these findings, we conclude that the proposed method has high performance in classification tasks with faster computational times than alternative methods, while it is less effective for prediction tasks were other methods achieve higher performance.en
dc.description.versionauthorsversion
dc.description.versioninpress
dc.format.extent1899605
dc.identifier.doi10.1109/ACCESS.2025.3593367
dc.identifier.issn2169-3536
dc.identifier.otherPURE: 129870384
dc.identifier.otherPURE UUID: 184c1c4e-4ef3-4be3-b46a-32d74658f58a
dc.identifier.otherScopus: 105012482635
dc.identifier.urihttp://hdl.handle.net/10362/188606
dc.identifier.urlhttps://www.scopus.com/pages/publications/105012482635
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50008%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de Projetos de I&D em Todos os Domínios Científicos - 2022 - ICDT/2022.08786.PTDC/PT
dc.subjectAnomalous SIP Dialogs
dc.subjectPerformance Analysis
dc.subjectSecurity
dc.subjectSession Initiation Protocol
dc.subjectGeneral Computer Science
dc.subjectGeneral Materials Science
dc.subjectGeneral Engineering
dc.titleCorrelation-Based Abnormal SIP Dialog Identificationen
dc.title.subtitleA Performance Comparison with Bayesian and Deep Learning Approachesen
dc.typejournal article
degois.publication.titleIEEE Access
dspace.entity.typePublication
oaire.awardNumberUIDB/50008/2020
oaire.awardNumber2022.08786.PTDC
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50008%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de Projetos de I&D em Todos os Domínios Científicos - 2022 - ICDT/2022.08786.PTDC/PT
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.fundingStreamConcurso de Projetos de I&D em Todos os Domínios Científicos - 2022 - ICDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicationada2f8e7-fadc-471b-b1a7-b1e35cba8ebc
relation.isProjectOfPublication29aefe76-9ee4-4230-b46d-0e5134b9907b
relation.isProjectOfPublication.latestForDiscovery29aefe76-9ee4-4230-b46d-0e5134b9907b

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