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dc.contributor.authorHagos, Desta Haileselassie
dc.contributor.authorEngelstad, Paal E.
dc.contributor.authorYazidi, Anis
dc.contributor.authorKure, Øivind
dc.date.accessioned2019-01-31T14:40:57Z
dc.date.accessioned2019-07-02T08:33:25Z
dc.date.available2019-01-31T14:40:57Z
dc.date.available2019-07-02T08:33:25Z
dc.date.issued2018-11-29
dc.identifier.citationHagos DH, Engelstad P.E., Yazidi A, Kure Ø: Recurrent Neural Network-Based Prediction of TCP Transmission States from Passive Measurements. In: IEEE .. 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), 2018. IEEE conference proceedings p. 1-10en
dc.identifier.isbn978-1-5386-7659-2
dc.identifier.urihttps://hdl.handle.net/10642/7244
dc.description.abstractLong Short-Term Memory (LSTM) neural networks are a state-of-the-art techniques when it comes to sequence learning and time series prediction models. In this paper, we have used LSTM-based Recurrent Neural Networks (RNN) for building a generic prediction model for Transmission Control Protocol (TCP) connection characteristics from passive measurements. To the best of our knowledge, this is the first work that attempts to apply LSTM for demonstrating how a network operator can identify the most important system-wide TCP per-connection states of a TCP client that determine a network condition (e.g., cwnd) from passive traffic measured at an intermediate node of the network without having access to the sender. We found out that LSTM learners outperform the state-of-the-art classical machine learning prediction models. Through an extensive experimental evaluation on multiple scenarios, we demonstrate the scalability and robustness of our approach and its potential for monitoring TCP transmission states related to network congestion from passive measurements. Our results based on emulated and realistic settings suggest that Deep Learning is a promising tool for monitoring system-wide TCP states from passive measurements and we believe that the methodology presented in our paper may strengthen future research work in the computer networking community.en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseries2018 IEEE 17th International Symposium on Network Computing and Applications (NCA);
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectLong Short-Term Memoriesen
dc.subjectTransmission control protocol congestion controlsen
dc.subjectPassive Measurementsen
dc.subjectRecurrent Neural Networksen
dc.titleRecurrent Neural Network-Based Prediction of TCP Transmission States from Passive Measurementsen
dc.typeChapter
dc.typeChapteren
dc.typePeer revieweden
dc.date.updated2019-01-31T14:40:57Z
dc.description.versionacceptedVersionen
dc.identifier.doihttp://dx.doi.org/10.1109/NCA.2018.8548064
dc.identifier.cristin1651113
dc.source.isbn978-1-5386-7659-2


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