Enhancing User Authentication via Deep Learning: A Keystroke Dynamics Approach

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Published Aug 9, 2024
https://doi.org/10.47164/ijngc.v15i2.1706
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Volume 15, Issue 2, July 2024

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Kartik N Iyer
a:1:{s:5:"en_US";s:37:"National Forensic Sciences University";}
https://orcid.org/0009-0002-4526-6569
Harsh K Upadhyay
https://orcid.org/0009-0009-8409-2224
Ravirajsinh S Vaghela
https://orcid.org/0000-0002-6303-9450

Abstract

User authentication is an important process that ensures only authorized individuals can access a system or network infrastructure. This process protects users’ sensitive information from unauthorized access and prevents any unwanted tampering. In this research paper, a unique method for authenticating users based on typing dynamics has been proposed, aimed to enhance the security of systems and networks by verifying user identities. This method involves calculating the period of time each key is pressed and released. The study includes data collection, feature extraction, model training, and performance evaluation by measuring the accuracy and precision of the trained model. We evaluated three deep-learning models to test the proposed method and determine its accuracy, precision, and superiority among the three models. Based on the findings of this research, we are able to present an algorithm which outperforms the other two considered for the experiment. Also, a comparative study is been presented after the first evaluation which involves assessing the accuracies for different lengths of the password. Additionally, charts and graphs were carefully employed to ensure precise representation and effective visualization of the data.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite
Iyer, K. N., Upadhyay, H. K., & Vaghela, R. S. . (2024). Enhancing User Authentication via Deep Learning: A Keystroke Dynamics Approach. International Journal of Next-Generation Computing, 15(2). https://doi.org/10.47164/ijngc.v15i2.1706
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