Imposter detection with canvas and WebGL using Machine learning.

Title

Imposter detection with canvas and WebGL using Machine learning.

Creator

Prathima M.S.; Milena S.P.; Rm P.

Description

Authentication offers a way to confirm the legitimacy of a user attempting to access any protected information that is hosted on the web as organizations are moving their applications online. It has long been believed that IP addresses and Cookies are the most reliable digital fingerprints used to authenticate and track people online. But after a while, things got out of hand when modern web technologies allowed interested organizations to use new ways to identify and track users. There are many new reliable digital fingerprints that can be used such as canvas and WebGL. The canvas and WebGL render the image which is dependent on the software and hardware of the system. In our work with the generated hash value value from canvas and WebGL we create a model using KNN to identify the imposters. The model has proved to be accurate in authentication of user with an accuracy of 89%. 2023 IEEE.

Source

2023 2nd International Conference for Innovation in Technology, INOCON 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Authentication; Canvas; fingerprinting; Machine Learning; WebGL

Coverage

Prathima M.S., Christ (Deemed to Be University) Lavasa, Department of Data Science, Maharashtra, Pune, India; Milena S.P., Christ (Deemed to Be University) Lavasa, Department of Data Science, Maharashtra, Pune, India; Rm P., Christ (Deemed to Be University) Lavasa, Department of Data Science, Maharashtra, Pune, India

Rights

Restricted Access

Relation

ISBN: 979-835032092-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/INOCON57975.2023.10101070

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158021326&doi=10.1109%2fINOCON57975.2023.10101070&partnerID=40&md5=4a3e2a948410b3d42b76df1432f2ab50