Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPEW34M/43BLG4E |
Repository | sid.inpe.br/sibgrapi/2020/10.01.17.17 |
Last Update | 2020:10.01.17.17.22 rodrigo.moreira@ufu.br |
Metadata | sid.inpe.br/sibgrapi/2020/10.01.17.17.22 |
Metadata Last Update | 2020:10.28.20.46.59 administrator |
Citation Key | MoreiraRodRosAguSil:2020:CoNeNe |
Title | Packet Vision: a convolutional neural network approach for network traffic classification  |
Format | On-line |
Year | 2020 |
Access Date | 2021, Jan. 25 |
Number of Files | 1 |
Size | 6474 KiB |
Context area | |
Author | 1 Moreira, Rodrigo 2 Rodrigues, Larissa Ferreira 3 Rosa, Pedro Frosi 4 Aguiar, Rui Luis Andrade 5 Silva, Flávio de Oliveira |
Affiliation | 1 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) |
Editor | Musse, Soraia Raupp Cesar Junior, Roberto Marcondes Pelechano, Nuria Wang, Zhangyang (Atlas) |
e-Mail Address | rodrigo.moreira@ufu.br |
Conference Name | Conference on Graphics, Patterns and Images, 33 (SIBGRAPI) |
Conference Location | Virtual |
Date | Nov. 7-10, 2020 |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2020-10-01 17:17:22 :: rodrigo.moreira@ufu.br -> administrator :: 2020-10-28 20:46:59 :: administrator -> rodrigo.moreira@ufu.br :: 2020 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Network traffic classification, convolutional neural networks, SDN, Network Slicing, data augmentation, fine-tuning. |
Abstract | Network 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. |
source Directory Content | there are no files |
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data URL | http://urlib.net/rep/8JMKD3MGPEW34M/43BLG4E |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/43BLG4E |
Language | en |
Target File | 17.pdf |
User Group | rodrigo.moreira@ufu.br |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/43G4L9S |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
Description control area | |
e-Mail (login) | rodrigo.moreira@ufu.br |
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