Název: DNS User Fingerprint
Autoři: SAYED, Karim
Typ dokumentu: Diplomové práce
Rok: 2025
Jazyk: eng
Abstrakt: In today's digital world, maintaining ones privacy has become a more difficult task. This paper explores whether advanced machine learning models can be utilized to fingerprint users can identify users based solely on data collected from a DNS server and to evalu- ate the aspects of a users behavior that can be used to identify them, compromising their privacy. By focusing on behavioral patterns and using feature engineering techniques along side time-series modeling of the users data, this work examines the privacy risks that can arise with DNS-based identification. This work will employ methods like mutual information, variance inflation factor (VIF), and Pearson correlation to select key features, testing them across models both sequential and non-sequential in an attempt to to identify the users. This research makes use of a publicly available dataset detailed in the paper "A user DNS fingerprint dataset", ensuring that the work can be compared with future studies and serve as a foundation for further research. Through time-series modeling and feature dimensionality reduction, an LSTM model was produced that is able to make predictions at 94% accuracy using only a subset of the pro- vided features. Additionally, non-temporal models were used to identify users at 79% accuracy and provide insights to what features impact the predictions the most.
Klíčová slova: DNS; time-series; User Fingerprinting
Citace: SAYED, Karim. DNS User Fingerprint. České Budějovice, 2025. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta

Instituce: Jihočeská univerzita v Českých Budějovicích (web)
Informace o dostupnosti dokumentu: Plný text je dostupný v digitálním repozitáři JČU.
Původní záznam: http://www.jcu.cz/vskp/77627

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-671164


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Vysokoškolské kvalifikační práce > Diplomové práce