Citation

Lee, Vicki Wei Qi (2024) Browser fingerprinting uniqueness identification using clustering methods and entropy validation. Masters thesis, Multimedia University.

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Abstract

A browser fingerprint is described as data gathered by the receiving server to identify a distant device and is totally different from cookies. Websites frequently utilise browser fingerprinting to obtain information about a browser's kind and version and the operating system, IP address, and other current settings. It is known that even when cookies are disabled, fingerprints can be used to identify users or devices fully or partially. The issue of browser fingerprinting has gained prominence in online privacy discussions, as there is currently no foolproof solution to prevent it completely. Existing solutions mainly focus on minimising the likelihood of browser fingerprinting. Research on browser fingerprinting is crucial to inform users, developers, policymakers, and law enforcement, enabling them to make informed decisions. Detecting and addressing browser fingerprinting is essential for privacy protection. This research paper emphasises the methodology of collecting data for browser fingerprinting, ensuring the acquisition of fingerprint data without compromising personal information. The objective is to transform this data into an easily accessible raw dataset for the industry's utilisation in future research projects Furthermore, this research primarily aims to identify the dominant attributes that can be effectively recognized through a machine learning clustering approach. The experimental results highlight the difficulty of clustering when dealing with highly distinct attributes. The study illuminates the challenges associated with grouping unique attributes using clustering techniques. Additionally, the study explores the potential use of Shannon Entropy to unveil distinctive attributes in browser fingerprinting, revealing that higher entropy values correlate with more distinct and recognisable fingerprints.

Item Type:	Thesis (Masters)
Additional Information:	Call No.: ZA4235 .L44 2024
Uncontrolled Keywords:	Web usage mining
Subjects:	Z Bibliography. Library Science. Information Resources > ZA3038-5190 Information resources (General) > ZA4050-4775 Information in specific formats or media > ZA4150-4380 Computer network resources
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Feb 2025 02:36
Last Modified:	03 Feb 2025 02:36
URII:	http://shdl.mmu.edu.my/id/eprint/13339

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