The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Issues and future directions in traffic classification
Abstract
Traffic classification technology has increased in relevance this decade, as it is now used in the definition and implementation of mechanisms for service differentiation, network design and engineering, security, accounting, advertising, and research. Over the past 10 years the research community and the networking industry have investigated, proposed and developed several classification approaches. While traffic classification techniques are improving in accuracy and efficiency, the continued proliferation of different Internet application behaviors, in addition to growing incentives to disguise some applications to avoid filtering or blocking, are among the reasons that traffic classification remains one of many open problems in Internet research. In this article we review recent achievements and discuss future directions in traffic classification, along with their trade-offs in applicability, reliability, and privacy. We outline the persistently unsolved challenges in the field over the last decade, and suggest several strategies for tackling these challenges to promote progress in the science of Internet traffic classification.
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“Furthermore, relying solely on port numbers has been shown to be ineffective [125,228,320], largely due to the use of dynamic port negotiation, tunneling and misuse of port numbers assigned to well-known applications for obfuscating traffic and avoiding firewalls [54,109,176,286].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [109]
In the citing paper
“Portbased techniques are unreliable and antiquated, largely due to the use of dynamic port negotiation, tunneling and misuse of port numbers assigned to well-known applications for obfuscating traffic and avoiding firewalls [54,109,176,286].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [109]
Citations
Cites
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Cited by
Sorted by citations
Deep packet: a novel approach for encrypted traffic classification using deep learning
Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., & Saberian, M. (2020) · Soft Computing · doi:10.1007/s00500-019-04030-2 · Cited by 1,190
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Malware traffic classification using convolutional neural network for representation learning
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Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2899085 · Cited by 525
Metadata
- Authors
- Dainotti, A., Pescape, A., & Claffy, K.
- Affiliations
- University of Naples Federico II (IT)
- ORCID
- Dainotti, Alberto
- Venue
- IEEE Network
- Published
- 2012-01
- Volume
- 26
- Issue
- 1
- Pages
- 35-40
- ISSN
- 0890-8044 (ISSN-L)
- OpenAlex
- W2149600645
- Cited by
- 630
- References
- 16
- Citations by year
- 2026: 24 · 2025: 41 · 2024: 43 · 2023: 46 · 2022: 65 · 2021: 48 · 2020: 54 · 2019: 43 · 2018: 43 · 2017: 34 · 2016: 47 · 2015: 44 · 2014: 46 · 2013: 39 · 2012: 12
- FWCI
- 44.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Network (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- network traffic classification, Internet traffic, privacy, service differentiation, network security, network accounting
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