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@InProceedings{MoreiraRodRosAguSil:2020:CoNeNe,
               author = "Moreira, Rodrigo and Rodrigues, Larissa Ferreira and Rosa, Pedro 
                         Frosi and Aguiar, Rui Luis Andrade and Silva, Fl{\'a}vio de 
                         Oliveira",
          affiliation = "{Federal University of Uberl{\^a}ndia - Faculty of Computing 
                         (FACOM)} and {Federal University of Vi{\c{c}}osa - Institute of 
                         Exact and Technological Sciences (IEP)} and {Federal University of 
                         Uberl{\^a}ndia - Faculty of Computing (FACOM)} and {University of 
                         Aveiro - Telecommunications Institute (IT)} and {Federal 
                         University of Uberl{\^a}ndia - Faculty of Computing (FACOM)}",
                title = "Packet Vision: a convolutional neural network approach for network 
                         traffic classification",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             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.",
  conference-location = "Virtual",
      conference-year = "Nov. 7-10, 2020",
             language = "en",
           targetfile = "17.pdf",
        urlaccessdate = "2020, Oct. 24"
}


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