<![CDATA[This research analyzes the effectiveness of the combination of User-Adaptive and Mahalanobis Distance methods in biometrics keystroke authentication systems. Using Biomey Keystroke Dataset with 40 respondents, this study aims to improve the accuracy and reliability of KD-based authentication. The developed system consists of enrollment and authentication stages, with User-Adaptive as the feature extraction method and Mahalanobis Distance for feature matching. Decision level fusion technique is applied to integrate the results of various keystroke features. The results obtained show that the fusion technique with Mahalanobis Distance shows better results compared to non-fusion features with an average error reduction of 8.73%. The optimal vector length (Fn) was found at n = 5 with an error value of 12.07%. The best threshold search resulted in a FAR of 15.6% and FRR of 6% at n = 5. The results obtained in this study show a lower error rate with an average decrease in error value of 9.9% with previous studies. This research proves the potential of Mahalanobis Distance and fusion techniques in improving the accuracy of biometrics keystroke authentication systems, opening up opportunities for the development of more reliable security systems. Further studies are recommended to explore certain patterns on the touch screen and the use of more varied datasets and real-time testing data. Keywords- authentication, biometrics]]>
Copyrights © 2024
