Implementation Issues of Early Application Identification
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With the rise in security and privacy concerns, payload is often encrypted and its access is prohibited due to privacy laws, making it non-trivial to infer a signature for an application class using payload.
“Furthermore, with the rise in security and privacy concerns, payload is often encrypted and its access is prohibited due to privacy laws.This makes it non-trivial to infer a signature for an application class using payload [54,138].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [54]
Relying solely on port numbers has been shown to be ineffective, 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.
“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 [54]
Port-based techniques are unreliable and antiquated, largely due to dynamic port negotiation, tunneling and misuse of port numbers assigned to well-known applications for obfuscating traffic and avoiding firewalls.
“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 [54]
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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 · see citing works
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Self-citationDynamic application-layer protocol analysis for network intrusion detection
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Automatic network traffic classification
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Use of machine learning in information security
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Automatic determination of types number of mixed binary protocols
Zhang, C., Wang, S., Zhan, D., & Yang, Y. (2019) · IET Communications · doi:10.1049/iet-com.2018.5185
Glisic, S., & Lorenzo, B. (2022) · Wiley eBooks · doi:10.1002/9781119790327.ch7
- Authors
- Bernaille, L., & Teixeira, R.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- ORCID
- Teixeira, Renata
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2007-10-31
- Pages
- 156-166
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1575180929
- Cited by
- 11
- References
- 14
- Citations by year
- 2022: 1 · 2021: 3 · 2019: 1 · 2018: 1 · 2013: 2 · 2012: 1
- FWCI
- 0.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- 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 › Software-Defined Networks and 5G
- Keywords
- network traffic classification, TCP connections, quality of service, memory optimization, computational optimization, edge networks
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