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Dynamics-Based Approach for Accurate User Identification and Authentication using Machine Learning Techniques

, Mouhammd Alkasassbeh, Ahmad Hassanat, Ahmad Al-Tarawneh
Published in
Pages: 1 - 10

This paper presents a methodology for improving the security of identification and authentication processes using Keystroke Dynamics (KSD). KSD is considered a behavioral biometric operating as a second level of security along with the login process after inserting user name and password. KSD is mainly about observing the way in which the user types. Firstly, we propose four new time features; these features represent the user’s behavior. Secondly, due to the unavailability of standard dataset, a new behavioral dataset is built. Thirdly, we propose employing KSD on CAPTCHA Code for the identification process. In this research, we applied three different classification techniques namely: J48, Random Forest and Multi-layer Perceptron (MLP), to accurately identify the user behavior (legitimate or illegitimate) and its authority. Random Forest showed the best result for the identification with accuracy (93.13%), however for the authorization process the highest accuracy was obtained using MLP (94.90%)

About the journal
JournalInternational Conference on Information Technology and Applications
Open AccessNo