research-article

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Coming of Age: A Longitudinal Study of TLS Deployment

Authors:

Platon Kotzias,

Abbas Razaghpanah,

Kenneth G. Paterson,

Narseo Vallina-Rodriguez,

Juan CaballeroAuthors Info & Claims

IMC '18: Proceedings of the Internet Measurement Conference 2018

Pages 415 - 428

https://doi.org/10.1145/3278532.3278568

Published: 31 October 2018 Publication History

Abstract

The Transport Layer Security (TLS) protocol is the de-facto standard for encrypted communication on the Internet. However, it has been plagued by a number of different attacks and security issues over the last years. Addressing these attacks requires changes to the protocol, to server- or client-software, or to all of them. In this paper we conduct the first large-scale longitudinal study examining the evolution of the TLS ecosystem over the last six years. We place a special focus on the ecosystem's evolution in response to high-profile attacks.

For our analysis, we use a passive measurement dataset with more than 319.3B connections since February 2012, and an active dataset that contains TLS and SSL scans of the entire IPv4 address space since August 2015. To identify the evolution of specific clients we also create the---to our knowledge---largest TLS client fingerprint database to date, consisting of 1,684 fingerprints.

We observe that the ecosystem has shifted significantly since 2012, with major changes in which cipher suites and TLS extensions are offered by clients and accepted by servers having taken place. Where possible, we correlate these with the timing of specific attacks on TLS. At the same time, our results show that while clients, especially browsers, are quick to adopt new algorithms, they are also slow to drop support for older ones. We also encounter significant amounts of client software that probably unwittingly offer unsafe ciphers. We discuss these findings in the context of long tail effects in the TLS ecosystem.

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IMC '18: Proceedings of the Internet Measurement Conference 2018

October 2018

507 pages

ISBN:9781450356190

DOI:10.1145/3278532

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Published: 31 October 2018

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https://doi.org/10.23919/TMA62044.2024.10559063
Tang KWu KChau SVilela JSchulmann HLi N(2024)Investigating TLS Version Downgrade in Enterprise SoftwareProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653263(31-42)Online publication date: 19-Jun-2024
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Theofanous APapadogiannaki EShevtsov AIoannidis SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Fingerprinting the Shadows: Unmasking Malicious Servers with Machine Learning-Powered TLS AnalysisProceedings of the ACM Web Conference 202410.1145/3589334.3645719(1933-1944)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645719
Sosnowski MZirngibl JSattler PCarle GGrohnfeldt CRusso MSgandurra D(2024)EFACTLS: Effective Active TLS Fingerprinting for Large-Scale Server Deployment CharacterizationIEEE Transactions on Network and Service Management10.1109/TNSM.2024.336452621:3(2582-2595)Online publication date: Jun-2024
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