1. Encrypted HTTP/2 Traffic Monitoring: Standing the Test of Time and Space
- Author
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Olivier Bettan, Pierre-Olivier Brissaud, Jerome Francois, Thibault Cholez, Isabelle Chrisment, Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems (RESIST), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Thales Services, THALES, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), THALES [France], ANR-19-CE39-0011,PRESTO,Traitement des flux chiffré s pour la gestion du trafic(2019), European Project: 830927,CONCORDIA(2019), and François, Jérôme
- Subjects
Service (systems architecture) ,Traffic analysis ,Computer science ,0211 other engineering and technologies ,Cryptography ,02 engineering and technology ,computer.software_genre ,Encryption ,Computer security ,[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,Traffic Analysis ,TLS ,0202 electrical engineering, electronic engineering, information engineering ,Set (psychology) ,021110 strategic, defence & security studies ,[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,business.industry ,020206 networking & telecommunications ,Test (assessment) ,Privacy ,Encrypted Traffic ,Web service ,business ,computer - Abstract
International audience; Encrypted HTTP/2 (h2) has been worldwide adopted since its official release in 2015. The major services over Internet use it to protect the user privacy against traffic interception. However, under the guise of privacy, one can hide the abnormal or even illegal use of a service. It has been demonstrated that machine learning algorithms combined with a proper set of features are still able to identify the incriminated traffic even when it is encrypted with h2. However, it can also be used to track normal service use and so endanger privacy of Internet users. Independently of the final objective, it is extremely important for a security practitioner to understand the efficiency of such a technique and its limit. No existing research has been achieved to assess how generic is it to be directly applicable to any service or website and how long an acceptable accuracy can be maintained. This paper addresses these challenges by defining an experimental methodology applied on more than 3000 different websites and also over four months continuously. The results highlight that an off-the-shelf machine-learning method to classify h2 traffic is applicable to many websites but a weekly training may be needed to keep the model accurate.
- Published
- 2020