Keystroke dynamics based user authentication using Histogram Gradient Boosting
ibrahim, M., AbdelRaouf, H., Amin, K., Semary, N. (2023). Keystroke dynamics based user authentication using Histogram Gradient Boosting. EKB Journal Management System, 10(1), 36-53. doi: 10.21608/ijci.2022.155605.1081
mina ibrahim; Hussien AbdelRaouf; Khalid M Amin; Noura Semary. "Keystroke dynamics based user authentication using Histogram Gradient Boosting". EKB Journal Management System, 10, 1, 2023, 36-53. doi: 10.21608/ijci.2022.155605.1081
ibrahim, M., AbdelRaouf, H., Amin, K., Semary, N. (2023). 'Keystroke dynamics based user authentication using Histogram Gradient Boosting', EKB Journal Management System, 10(1), pp. 36-53. doi: 10.21608/ijci.2022.155605.1081
ibrahim, M., AbdelRaouf, H., Amin, K., Semary, N. Keystroke dynamics based user authentication using Histogram Gradient Boosting. EKB Journal Management System, 2023; 10(1): 36-53. doi: 10.21608/ijci.2022.155605.1081

Keystroke dynamics based user authentication using Histogram Gradient Boosting

IJCI. International Journal of Computers and Information
Article 4, Volume 10, Issue 1, January 2023, Page 36-53  XML PDF (1.47 MB)
Document Type: Original Article
DOI: 10.21608/ijci.2022.155605.1081
View on SCiNiTO View on SCiNiTO
Authors
mina ibrahim1; Hussien AbdelRaouf email 1; Khalid M Aminorcid 2; Noura Semary1
1Department of Information Technology, Faculty of Computer and Information, Menoufia University, Egypt
2Information Technology dept., Faculty of Computer and Information, Menoufia University, Egypt
Abstract
User authentication is a vital part of securing digital services and preventing unauthorized users from gaining access to the system. Nowadays, organizations use Multi-Factor Authentication (MFA) to provide robust protection by utilizing two or more identity procedures instead of using Single Factor Authentication (SFA) which became less secure. Keystroke dynamics is a behavioral biometric that examines a user’s typing rhythm to determine the subject’s legitimacy using the system. Keystroke dynamics have a minimal implementation cost and
do not need special hardware in the authentication process since the gathering of typing data is reasonably straightforward and does not involve additional effort from the user. In this
work, we present an efficient approach that uses the quantile transformation that transforms data distribution into uniform distribution which significantly reduces the impact of outlier and
extreme values. Histogram Gradient Boosting is employed as the primary classifier for the training and testing phase. Our proposed approach is evaluated on Carnegie Mellon University
(CMU) keystroke benchmark dataset which has achieved 97.96% of average accuracy and 0.014 of average equal error rate (EER) across all subjects which outperforms all the previous advances in both machine and deep learning approaches.
Keywords
Keystroke Dynamics; Authentication; Histogram Gradient Boosting; CMU; Machine Learning
Statistics
Article View: 427
PDF Download: 536