A multi-level framework to identify HTTPS services
Open accessAbstract
The development of TLS-based encrypted traffic comes with new challenges related to the management and security analysis of encrypted traffic. There is an essential need for new methods to investigate, with a proper level of identification, the increasing number of HTTPS traffic that may hold security breaches. In fact, although many approaches detect the type of an application (Web, P2P, SSH, etc.) running in secure tunnels, and others identify a couple of specific encrypted web pages through website fingerprinting, this paper proposes a robust technique to precisely identify the services run within HTTPS connections, i.e. to name the services, without relying on specific header fields that can be easily altered. We have defined dedicated features for HTTPS traffic that are used as input for a multi-level identification framework based on machine learning algorithms. Our evaluation based on real traffic shows that we can identify encrypted web services with a high accuracy.
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RF performs better than C4.5 DT with a precision of 92.6%, recall of 92.8%, and F-measure of 92.6%, to classify service providers with selected features.
“In the evaluation, RF performs better in comparison to C4.5 DT with a precision of 92.6%, recall of 92.8%, and F-measure of 92.6%, to classify service providers with selected features.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [409]
Shbair et al. [409] adopt a hierarchical classification to identify the service provider followed by the type of service encapsulated in TLS-based HTTPS sessions.
“[409] by adopting a hierarchical classification to identify the service provider (e.g.google.com,dropbox.com),followed by the type of service (e.g.maps.google.com,drive.google.com) that are encapsulated in TLS-based HTTPS sessions.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [409]
A hierarchical approach to traffic classification reduces the effort required to retrain classifiers when a new service is added.
“This hierarchical approach reduces the effort required to retrain the classifiers in the event of an addition of a new service.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [409]
The accuracy of service classification is between 95-100% for the majority of providers.
“Furthermore, the accuracy of service classification is between 95-100% for majority of the providers.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [409]
The overall accuracy of Shbair et al.'s system across both levels is 93.10% with a degradation of less than 20% over a period of 23 weeks without retraining.
“Also, overall accuracy of the system across both levels is 93.10% with a degradation of less than 20% over a period of 23 weeks without retraining.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [409]
A hierarchical method that identifies service provider followed by type of service, using statistical features from encrypted payload, has been highly accurate and temporally stable.
“For instance, a hierarchical method that identifies service provider followed by type of service, using statistical features from encrypted payload has been highly accurate and temporally stable [409].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [409]
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- Authors
- Shbair, W. M., Cholez, T., Francois, J., & Chrisment, I.
- Affiliations
- Laboratoire Lorrain de Recherche en Informatique et ses Applications (FR)
- Venue
- NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
- Publisher
- IEEE
- Published
- 2016-04
- Pages
- 240-248
- OpenAlex
- W2469987047
- Cited by
- 69
- References
- 29
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 7 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 7 · 2019: 11 · 2018: 8 · 2017: 5 · 2016: 1
- FWCI
- 4.49
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- Locations
- IEEE/IFIP Network Operations and Management Symposium (NOMS) (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Information Systems › Web Application Security Vulnerabilities
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- HTTPS traffic, service identification, encrypted traffic analysis, TLS, machine learning features, web service classification, network security
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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