Close

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43BLG4E
Repositorysid.inpe.br/sibgrapi/2020/10.01.17.17
Last Update2020:10.01.17.17.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/10.01.17.17.22
Metadata Last Update2022:06.14.00.00.16 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00042
Citation KeyMoreiraRodRosAguSil:2020:CoNeNe
TitlePacket Vision: a convolutional neural network approach for network traffic classification
FormatOn-line
Year2020
Access Date2024, Apr. 27
Number of Files1
Size6474 KiB
2. Context
Author1 Moreira, Rodrigo
2 Rodrigues, Larissa Ferreira
3 Rosa, Pedro Frosi
4 Aguiar, Rui Luis Andrade
5 Silva, Flávio de Oliveira
Affiliation1 Federal University of Uberlândia - Faculty of Computing (FACOM)  
2 Federal University of Viçosa - Institute of Exact and Technological Sciences (IEP)  
3 Federal University of Uberlândia - Faculty of Computing (FACOM)  
4 University of Aveiro - Telecommunications Institute (IT)  
5 Federal University of Uberlândia - Faculty of Computing (FACOM)
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressrodrigo.moreira@ufu.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-10-01 17:17:22 :: rodrigo.moreira@ufu.br -> administrator ::
2022-03-07 03:12:20 :: administrator -> banon :: 2020
2022-03-07 03:13:35 :: banon -> administrator :: 2020
2022-03-08 03:07:31 :: administrator -> banon :: 2020
2022-03-08 03:14:29 :: banon -> administrator :: 2020
2022-06-14 00:00:16 :: administrator -> rodrigo.moreira@ufu.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsNetwork traffic classification
convolutional neural networks
SDN
Network Slicing
data augmentation
fine-tuning
AbstractNetwork traffic classification can improve the management and network service offer, taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets which can transform them into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach surpasses those found in the state-of-the-art, considering classification performance and regarding the fully-packet structure characteristic, delivering security and privacy by transforming the raw-data packet into images. Besides, we built a dataset with four traffic classes and evaluated three CNNs architectures, considering performance and the exploitation of training from scratch, fine-tuning and hyperparameter optimization. Experiments showcase applicability and suitability when combining Packet Vision with CNNs, which seemed to be a promising approach to deliver outstanding performance in the classification of network traffic.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > Packet Vision: a...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Packet Vision: a...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 01/10/2020 14:17 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43BLG4E
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43BLG4E
Languageen
Target File17.pdf
User Grouprodrigo.moreira@ufu.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
e-Mail (login)rodrigo.moreira@ufu.br
update 


Close