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@inproceedings{1420614, author = {Laštovička, Martin and Dufka, Antonín and Komárková, Jana}, address = {Limassol, Cyprus}, booktitle = {Proceedings of the 14th International Wireless Communications and Mobile Computing Conference}, doi = {http://dx.doi.org/10.1109/IWCMC.2018.8450406}, editor = {IEEE}, keywords = {Machine Learning; OS Fingerprinting; IPFIX; Cybersecurity}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Limassol, Cyprus}, isbn = {978-1-5386-2070-0}, pages = {542-547}, publisher = {IEEE Xplore Digital Library}, title = {Machine Learning Fingerprinting Methods in Cyber Security Domain: Which one to Use?}, url = {https://ieeexplore.ieee.org/abstract/document/8450406}, year = {2018} }
TY - JOUR ID - 1420614 AU - Laštovička, Martin - Dufka, Antonín - Komárková, Jana PY - 2018 TI - Machine Learning Fingerprinting Methods in Cyber Security Domain: Which one to Use? PB - IEEE Xplore Digital Library CY - Limassol, Cyprus SN - 9781538620700 KW - Machine Learning KW - OS Fingerprinting KW - IPFIX KW - Cybersecurity UR - https://ieeexplore.ieee.org/abstract/document/8450406 L2 - https://ieeexplore.ieee.org/abstract/document/8450406 N2 - Identification of a communicating device operating system is a fundamental part of network situational awareness. However, current networks are large and change often which implies the need for a system that will be able to continuously monitor the network and handle changes in identified operating systems. The aim of this paper is to compare machine learning methods performance for OS fingerprinting on real-world data in the terms of processing time, memory requirements, and performance measures of accuracy, precision, and recall. ER -
LAŠTOVIČKA, Martin, Antonín DUFKA a Jana KOMÁRKOVÁ. Machine Learning Fingerprinting Methods in Cyber Security Domain: Which one to Use? In IEEE. \textit{Proceedings of the 14th International Wireless Communications and Mobile Computing Conference}. Limassol, Cyprus: IEEE Xplore Digital Library, 2018. s.~542-547. ISBN~978-1-5386-2070-0. doi:10.1109/IWCMC.2018.8450406.
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