The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Unexpected means of protocol inference
Abstract
Network managers are inevitably called upon to associate network traffic with particular applications. Indeed, this operation is critical for a wide range of management functions ranging from debugging and security to analytics and policy support. Traditionally, managers have relied on application adherence to a well established global port mapping: Web traffic on port 80, mail traffic on port 25 and so on. However, a range of factors - including firewall port blocking, tunneling, dynamic port allocation, and a bloom of new distributed applications - has weakened the value of this approach. We analyze three alternative mechanisms using statistical and structural content models for automatically identifying traffic that uses the same application-layer protocol, relying solely on flow content. In this manner, known applications may be identified regardless of port number, while traffic from one unknown application will be identified as distinct from another. We evaluate each mechanism's classification performance using real-world traffic traces from multiple sites.
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In the citing paper
“These models are good at inferring discriminators for binary, textual and structural content [286].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [286]
In the citing paper
“Ma et al. [286] show that payload-based traffic classification can be performed without any a priori knowledge of the application classes using unsupervised clustering.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [286]
In the citing paper
“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 [286]
In the citing paper
“In evaluation, the PD-based protocol models resulted in the lowest total misclassification error, under 5%.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [286]
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Metadata
- Authors
- Ma, J., Levchenko, K., Kreibich, C., Savage, S., & Voelker, G. M.
- Affiliations
- University of California San Diego (US)
- University of Cambridge (GB)
- Venue
- Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
- Publisher
- ACM
- Published
- 2006-10-25
- Pages
- 313-326
- OpenAlex
- W2148346742
- Cited by
- 204
- References
- 22
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 4 · 2022: 9 · 2021: 5 · 2020: 11 · 2019: 4 · 2018: 7 · 2017: 10 · 2016: 6 · 2015: 12 · 2014: 12 · 2013: 16 · 2012: 17
- FWCI
- 11.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Internet Measurement Conference (IMC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- network traffic classification, structural-content model, port mapping, tunneling, network management, traffic traces
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 ›
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