The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Survey on Internet Traffic Identification
Abstract
The area of Internet traffic measurement has advanced enormously over the last couple of years. This was mostly due to the increase in network access speeds, due to the appearance of bandwidth-hungry applications, due to the ISPs' increased interest in precise user traffic profile information and also a response to the enormous growth in the number of connected users. These changes greatly affected the work of Internet service providers and network administrators, which have to deal with increasing resource demands and abrupt traffic changes brought by new applications. This survey explains the main techniques and problems known in the field of IP traffic analysis and focuses on application detection. First, it separates traffic analysis into packet-based and flow-based categories and details the advantages and problems for each approach. Second, this work cites the techniques for traffic analysis accessible in the literature, along with the analysis performed by the authors. Relevant techniques include signature-matching, sampling and inference. Third, this work shows the trends in application classification analysis and presents important and recent references in the subject. Lastly, this survey draws the readers' interest to open research topics in the area of traffic analysis and application detection and makes some final remarks.
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Cites
Bro: a system for detecting network intruders in real-time
Paxson, V. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00112-7 · Cited by 2,636
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
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
Karagiannis, 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
Roughan, 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
Early application identification
Bernaille, L., Teixeira, R., & Salamatian, K. (2006) · Proceedings of the 2006 ACM CoNEXT conference on - CoNEXT '06 · doi:10.1145/1368436.1368445 · Cited by 374
Cited by
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
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
When Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211
Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic
Nguyen, T. T. T., Armitage, G., Branch, P., & Zander, S. (2012) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2012.2187305 · Cited by 156
Exploiting Diversity in Android TLS Implementations for Mobile App Traffic Classification
Sengupta, S., Ganguly, N., De, P., & Chakraborty, S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313738 · Cited by 45
“Unlike in the case of traditional web traffic, mobile app traffic cannot be segregated based on the presence of obvious signatures (e.g., server address, app port number, unique identifiers in packet content, etc. [12]).”
Background
Metadata
- Authors
- Callado, A., Kamienski, C., Szabo, G., Gero, B. P., Kelner, J., Fernandes, S., & Sadok, D.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2009
- Volume
- 11
- Issue
- 3
- Pages
- 37-52
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2120948237
- Cited by
- 378
- References
- 6
- FWCI
- 19.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- Callado, A., Kamienski, C., Szabo, G., Gero, B. P., Kelner, J., Fernandes, S., & Sadok, D. (2009). A Survey on Internet Traffic Identification. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/surv.2009.090304
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