Abstract—User authentication is an essential factor to protect digital service and prevent malicious users from gaining access to the system. As Single Factor Authentication (SFA) is less secure, organizations started to utilize Multi-Factor Authentication (MFA) to provide reliable protection by using two or more identification measures. Keystroke dynamics is a behavioral biometric, which analyses users typing rhythm to identify the legitimacy of the subject accessing the system. Keystroke dynamics that have a low implementation cost and does not require additional hardware in the authentication process since the collection of typing data is relatively simple as it does not require extra effort from the user. This study aims to propose deep learning model using Multilayer Perceptron (MLP) in keystroke dynamics for user authentication on CMU benchmark dataset. The user typing rhythm from 51 subjects collected based on the static password (.tie5Roanl) typed 400 times over 8 sessions and 50 repetitions per session. The MLP achieved optimum EER of 4.45% compared to original benchmark classifiers such as 9.6% (scaled Manhattan), 9.96% (Mahalanobis Nearest Neighbor), 10.22% (Outlier Count), 10.25% and 16.14% (Neural Network Auto-Assoc).
Index Terms—Authentication, behavioral biometrics, deep learning, keystroke dynamics, multilayer perceptron.
Alvin Andrean and Vinesh Thiruchelvam are with Asia Pacific University of Technology & Innovation, Technology Park Malaysia, Kuala Lumpur 57000, Malaysia (e-mail: alvinsforz@gmail.com, dr.vinesh@apu.edu.my).
Manoj Jayabalan is with Liverpool John Moores University, Liverpool L3 3AF, United Kingdom (e-mail: m.jayabalan@ljmu.ac.uk).
Cite: Alvin Andrean, Manoj Jayabalan, and Vinesh Thiruchelvam, "Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 134-139, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).