Abnormal Behavior Detection Based on Traffic Pattern Categorization in Mobile Cellular Networks

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dc.contributor.authorDe Almeida, J. M.en
dc.contributor.authorPontes, C. F. T.en
dc.contributor.authorDaSilva, Luiz A.en
dc.contributor.authorBoth, C. B.en
dc.contributor.authorGondim, J. J. C.en
dc.contributor.authorRalha, Celia G.en
dc.contributor.authorMarotta, M. A.en
dc.date.accessioned2022-01-04T19:46:36Zen
dc.date.available2022-01-04T19:46:36Zen
dc.date.issued2021-01-01en
dc.date.updated2022-01-04T19:46:33Zen
dc.description.abstractAbnormal behavior in mobile cellular networks can cause network faults and consequent cell outages, a major reason for operational cost increase and revenue loss for operators. Nonetheless, network faults and cell outages can be avoided by monitoring abnormal situations in the network and acting accordingly. Thus, anomaly detection is an important component of self-healing control and network management. Network operators may use the detected abnormal behavior to quantify numerically their intensity. The quantification of abnormal behavior assists the characterization of potential regions for infrastructure updates and to support the creation of public policies for local connectivity enhancements. We propose an unsupervised learning solution for anomaly detection in mobile networks using Call Detail Records (CDR) data. We evaluate our solution using a real CDR data set provided by an Italian operator and compare it against other state-of-the-art solutions, showing a performance improvement of around 35%. We also demonstrate the relevance of considering the distinct traffic patterns of diverging geographic areas for anomaly detection in mobile networks, an aspect often ignored in the literature.en
dc.description.versionAccepted versionen
dc.format.extentPages 4213-4224en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TNSM.2021.3125019en
dc.identifier.eissn1932-4537en
dc.identifier.issn1932-4537en
dc.identifier.issue4en
dc.identifier.urihttp://hdl.handle.net/10919/107357en
dc.identifier.volume18en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectNetworking & Telecommunicationsen
dc.subject0805 Distributed Computingen
dc.subject0906 Electrical and Electronic Engineeringen
dc.subject1005 Communications Technologiesen
dc.titleAbnormal Behavior Detection Based on Traffic Pattern Categorization in Mobile Cellular Networksen
dc.title.serialIEEE Transactions on Network and Service Managementen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen

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