The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
BLINC: multilevel traffic classification in the dark
Abstract
We present a fundamentally different approach to classifying traffic flows according to the applications that generate them. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. We analyze these patterns at three levels of increasing detail (i) the social, (ii) the functional and (iii) the application level. This multilevel approach of looking at traffic flow is probably the most important contribution of this paper. Furthermore, our approach has two important features. First, it operates in the dark, having (a) no access to packet payload, (b) no knowledge of port numbers and (c) no additional information other than what current flow collectors provide. These restrictions respect privacy, technological and practical constraints. Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows. We demonstrate the effectiveness of our approach on three real traces. Our results show that we are able to classify 80%-90% of the traffic with more than 95% accuracy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Toward the Accurate Identification of Network Applications
Moore, A. W., & Papagiannaki, K. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31966-5_4 · Cited by 801
Self-citationKaragiannis, T., Broido, A., Brownlee, N., Claffy, K. C., & Faloutsos, M. (2005) · IEEE Global Telecommunications Conference, 2004. GLOBECOM '04. · doi:10.1109/glocom.2004.1378239 · Cited by 504
Self-citationRoughan, M., Sen, S., Spatscheck, O., & Duffield, N. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028805 · Cited by 494
Cited by
A survey of techniques for internet traffic classification using machine learning
Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
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
Traffic classification on the fly
Bernaille, L., Teixeira, R., Akodkenou, I., Soule, A., & Salamatian, K. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1129582.1129589 · Cited by 585
Bayesian Neural Networks for Internet Traffic Classification
Auld, T., Moore, A. W., & Gull, S. F. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883010 · Cited by 494
Metadata
- Authors
- Karagiannis, T., Papagiannaki, K., & Faloutsos, M.
- Venue
- Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
- Publisher
- ACM
- Published
- 2005-08-22
- Pages
- 229-240
- OpenAlex
- W2122226347
- Cited by
- 997
- References
- 3
- FWCI
- 45.79
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: BLINC: multilevel traffic classification in the dark. https://app.cochat.ai/papers/10.1145/1080091.1080119
- The paper
- Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005). BLINC: multilevel traffic classification in the dark. Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications. https://doi.org/10.1145/1080091.1080119
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.
