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Proficient User Authentication based on the Dynamic keystroke using Machine Learning

Charles, Trecy Soumya (2022) Proficient User Authentication based on the Dynamic keystroke using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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The authentication conditions of today go beyond PINs and passwords. It needs a significant amount of Keystroke biometrics can offer security. even though they have the login. This study attempts to identify a legitimate user using his information. The paper's objective is to examine the various approaches and reach an agreement. By including a keystroke mechanism, The existing setup increases safety. In the industry of information technology, which is quickly growing User authentication is one of the time-consuming tasks of today. Everyone must employ swift as well as safe authentication. We developed and implemented the keystroke to address this problem technology. Keystroke data may be successfully used to imitate user input, which is our main assumption behavior. Utilizing a neighbor key pattern enhances the precision of user identification and assists in identifying legitimate users and pretenders. A few significant keystroke variables have been used to establish the User Type. The space between successive strokes of the user identifying characters is utilized in this method. using behavioral characteristics like the typing habits of multiple users. Executing a Combining parameters like recall, F1, accuracy, precision, and run time in comparison analysis is a important part of the research.

Item Type: Thesis (Masters)
Pantridge, Micheal
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 28 Apr 2023 13:55
Last Modified: 28 Apr 2023 13:55

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