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Toward the Accurate Identification of Network Applications
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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 [320]
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Snort - Lightweight Intrusion Detection for Networks
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Packet-level traffic measurements from the sprint IP backbone
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Self-citationContent-aware Internet application traffic measurement and analysis
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Metadata
- Authors
- Moore, A. W., & Papagiannaki, K.
- Affiliations
- University of Cambridge (GB)
- Intel (United States) (US)
- ORCID
- Moore, Andrew W.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2005
- Pages
- 41-54
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1521553548
- Cited by
- 801
- References
- 6
- Citations by year
- 2026: 10 · 2025: 26 · 2024: 31 · 2023: 25 · 2022: 40 · 2021: 35 · 2020: 41 · 2019: 34 · 2018: 36 · 2017: 28 · 2016: 31 · 2015: 35 · 2014: 39 · 2013: 48 · 2012: 56
- FWCI
- 25.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Intel Corporation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- network traffic classification, content-based classification, packet payload analysis, firewall bypass, network traffic measurement, packet traces
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