Citation

Lee, Vicki Wei Qi and Ooi, Shih Yin and Pang, Ying Han (2023) Assessing the Importance of Browser Fingerprint Attributes towards User Profiling through Clustering Algorithms. In: 2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 20-21 May 2023, Penang, Malaysia.

Text 3.pdf - Published Version Restricted to Repository staff only Download (415kB)

Abstract

Browser fingerprint is often linked to privacy as it is a method to gather data about the configuration of the browser to identify a user. The browser’s configurations which are also known as attributes are the key to making the user to be identified. Web browsers explicitly disclose information about the host system to websites by making that information available to them, such as attributes like the screen resolution, local time, or OS version. Since each browser has its different attributes values and that will make each of them unique and so it is important to understand well about the attributes. In this research, the purpose is to discover the dominant attributes which are highly identifiable via the clustering algorithm. Experiment results showed that if the attribute is unique, it will be hard to cluster into groups. This can be proved by using a clustering algorithm on WEKA platform.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Browser fingerprint, attributes, clustering algorithm, WEKA
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare. Criminology > HV6001-7220.5 Criminology > HV6035-6197 Criminal anthropology Including criminal types, criminal psychology, prison psychology, causes of crime
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Jul 2023 03:03
Last Modified:	31 Jul 2023 03:03
URII:	http://shdl.mmu.edu.my/id/eprint/11572

Downloads

Edit (login required)