Desktop and Mobile Operating System Fingerprinting based on IPv6 Protocol using Machine Learning Algorithms
Date
2021
Authors
Salah, Saeed
Abu Alhawa, Mohammad
Zaghal, Raid
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Security and Networks, InderScience
Abstract
Operating system (OS) fingerprinting tools are essential to network security because
of their relationship to vulnerability scanning and penetrating testing. Although OS identification
is traditionally performed by passive or active tools, more contributions have focused on IPv4
than IPv6. This paper proposes a new methodology based on machine learning algorithms to
build classification models to identify IPv6 OS fingerprinting using a newly created dataset.
Unlike other proposals that mainly depend on TCP and IP generic features; this work adds other
features to improve the detection accuracy. It also considers OSes installed in mobiles (Android
and iOS). The experimental results have shown that the algorithms achieved high and acceptable
results in classifying OSes. KNN and DT achieved high accuracy of up to 99%. SVM and GNB
achieved 81% and 75%, respectively. Moreover, KNN, RF and DT achieved the best recall,
precision, and f-score with almost the same as the achieved accuracy.
Description
Keywords
operating system , fingerprinting , IPv6 , network security , machine learning , mobile operating system , performance measures
Citation
S. Salah, M. Abu Alhawa, and Raid Zaghal, “Desktop and Mobile Operating System Fingerprinting based on IPv6 Protocol using Machine Learning Algorithms”, International Journal of Security and Networks, InderScience, 2021.