2020 29th International Conference on Computer Communications and Networks (ICCCN) 2020
DOI: 10.1109/icccn49398.2020.9209694
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Advanced Passive Operating System Fingerprinting Using Machine Learning and Deep Learning

Abstract: without whom my Ph.D. life in Oslo would not have been so much fun. I am deeply grateful for all the fun, jokes and sometimes serious academic conversations over our lunch and dinner times. You all have been the source of my laughter and positive energy ever since we became colleagues. I have had so much fun along the way and my sincere thanks to you all buddies. There are still other very good friends, families, and colleagues, too many to list here, who I have had the opportunity to discuss ideas about my Ph… Show more

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Cited by 3 publications
(1 citation statement)
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“…The datasets and the research works were published between 2018 and 2020 and should accurately represent current real-world network traffic as the datasets contain network flow capture from large-scale campus network including the OS labels from various OS detection techniques up to the minor OS version detail level. The datasets were recently used by Fan et al [53], Hagos et al [72,73], and Zhang et al [91] from different research groups, which indicates the usability of the dataset. Moreover, the usability of the datasets is supported by more the 500 downloads of the datasets.…”
Section: Datasets and Fingerprinting Featuresmentioning
confidence: 99%
“…The datasets and the research works were published between 2018 and 2020 and should accurately represent current real-world network traffic as the datasets contain network flow capture from large-scale campus network including the OS labels from various OS detection techniques up to the minor OS version detail level. The datasets were recently used by Fan et al [53], Hagos et al [72,73], and Zhang et al [91] from different research groups, which indicates the usability of the dataset. Moreover, the usability of the datasets is supported by more the 500 downloads of the datasets.…”
Section: Datasets and Fingerprinting Featuresmentioning
confidence: 99%