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@inproceedings{39018,
author = {Laštovička, Martin and Špaček, Stanislav and Velan, Petr and Čeleda, Pavel},
address = {Budapešť, Maďarsko},
booktitle = {2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)},
doi = {http://dx.doi.org/10.1109/NOMS47738.2020.9110319},
keywords = {OS fingerprinting;passive monitoring;IPFIX;TLS},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Budapešť, Maďarsko},
isbn = {978-1-7281-4973-8},
pages = {1-6},
publisher = {IEEE Xplore Digital Library},
title = {Using TLS Fingerprints for OS Identification in Encrypted Traffic},
url = {https://ieeexplore.ieee.org/document/9110319},
year = {2020}
}
TY - JOUR
ID - 39018
AU - Laštovička, Martin - Špaček, Stanislav - Velan, Petr - Čeleda, Pavel
PY - 2020
TI - Using TLS Fingerprints for OS Identification in Encrypted Traffic
PB - IEEE Xplore Digital Library
CY - Budapešť, Maďarsko
SN - 9781728149738
KW - OS fingerprinting;passive monitoring;IPFIX;TLS
UR - https://ieeexplore.ieee.org/document/9110319
N2 - Asset identification plays a vital role in situational awareness building. However, the current trends in communication encryption and the emerging new protocols turn the well-known methods into a decline as they lose the necessary data to work correctly. In this paper, we examine the traffic patterns of the TLS protocol and its changes introduced in version 1.3. We train a machine learning model on TLS handshake parameters to identify the operating system of the client device and compare its results to well-known identification methods. We test the proposed method in a large wireless network. Our results show that precise operating system identification can be achieved in encrypted traffic of mobile devices and notebooks connected to the wireless network.
ER -
LAŠTOVIČKA, Martin, Stanislav ŠPAČEK, Petr VELAN a Pavel ČELEDA. Using TLS Fingerprints for OS Identification in Encrypted Traffic. Online. In \textit{2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)}. Budapešť, Maďarsko: IEEE Xplore Digital Library, 2020, s.~1-6. ISBN~978-1-7281-4973-8. Dostupné z: https://dx.doi.org/10.1109/NOMS47738.2020.9110319.
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