The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Independent comparison of popular DPI tools for traffic classification
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Cites
Issues and future directions in traffic classification
Dainotti, A., Pescape, A., & Claffy, K. (2012) · IEEE Network · doi:10.1109/mnet.2012.6135854 · Cited by 630
Internet traffic classification demystified: myths, caveats, and the best practices
Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., & Lee, K. (2008) · Proceedings of the 2008 ACM CoNEXT Conference on - CONEXT '08 · doi:10.1145/1544012.1544023 · Cited by 485
Research works printed but not linked: 7
- Khalife, 2013, Performance of OpenDPI in identifying sampled network traffic, J. Netw., 8, 71
- J. Khalife, A. Hajjar, J. Díaz-Verdejo, On the performance of OpenDPI in identifying P2P truncated flows, in: AP2PS 2011, The Third International Conference on Advances in P2P Systems, IARIA, Lisbon, Portugal, 2011, pp. 79–84.
- Gringoli, 2009, Gt: picking up the truth from the ground for internet traffic, ACM SIGCOMM Comput. Commun. Rev., 39, 12, 10.1145/1629607.1629610 · DOI · Paper page
- Daniele De Sensi, Marco Danelutto, Luca Deri, DPI Over Commodity Hardware: Implementation of a Scalable Framework using FastFlow, Master’s Thesis, Universitá di Pisa, Italy, 2012. <http://etd.adm.unipi.it/t/etd-02042013-101033/>.
- Bujlow, 2012, Volunteer-based system for research on the Internet traffic, TELFOR J., 4, 2
- Carela-Español, 2011, Analysis of the impact of sampling on NetFlow traffic classification, Comput. Netw., 55, 1083, 10.1016/j.comnet.2010.11.002 · DOI
- Dusi, 2011, Quantifying the accuracy of the ground truth associated with Internet traffic traces, Comput. Netw., 55, 1158, 10.1016/j.comnet.2010.11.006 · DOI
Cited by
Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things
Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017) · IEEE Access · doi:10.1109/access.2017.2747560 · Cited by 767
FS-Net: A Flow Sequence Network For Encrypted Traffic Classification
Liu, C., He, L., Xiong, G., Cao, Z., & Li, Z. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737507 · Cited by 630
Lin, X., Xiong, G., Gou, G., Li, Z., Shi, J., & Yu, J. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512217 · Cited by 599
Robust Smartphone App Identification via Encrypted Network Traffic Analysis
Taylor, V. F., Spolaor, R., Conti, M., & Martinovic, I. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737970 · Cited by 439
“[9] do an independent comparison of traffic”
A survey of methods for encrypted traffic classification and analysis
Velan, P., Čermák, M., Čeleda, P., & Drašar, M. (2015) · International Journal of Network Management · doi:10.1002/nem.1901 · Cited by 377
Metadata
- Authors
- Bujlow, T., Carela-Español, V., & Barlet-Ros, P.
- Venue
- Computer Networks
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 76
- Pages
- 75-89
- ISSN
- 1389-1286 (ISSN-L)
- OpenAlex
- W2148913232
- Cited by
- 245
- References
- 2
- FWCI
- 10.23
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- Spanish Ministry of Science and Innovation (TEC2011-27474)
- AGAUR (2014-SGR-1427)
- Aalborg University (8-10100)
- European Regional Development Fund
- Bredbånd Nord A/S
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- The paper
- Bujlow, T., Carela-Español, V., & Barlet-Ros, P. (2015). Independent comparison of popular DPI tools for traffic classification. Computer Networks. https://doi.org/10.1016/j.comnet.2014.11.001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
