KEYSTROKE DYNAMICS BASED MULTI-FACTOR AUTHENTICATION USING MACHINE LEARNING

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 behavioural 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 research we used the CMU fixed benchmark data set of 20400 sizes which is used for keystroke dynamics. The data set collects 51 users’ keystroke dynamics information where each user typed the same password. .tie5Roanl 400 times over 8 sessions and there are 50 repetitions in each session. We tested four different machine learning algorithms: Random Forest, Support Vector Machines, Multi-Layer Perceptron and Extra Trees, to determine which algorism is most effective on accuracy. We also tested these four algorithms with respect to Accuracy, Precision, Recall and F1 score evaluation matrix to compare the performance. The random forest classifier scores extremely high accuracy (99.19%) and with these final results, we can determine what method of machine learning is most effective at accurately authenticating users.