User authentication through behavioral biometrics using multi-class classification algorithms : A comprehensive study of machine learning algorithms for keystroke and mouse dynamics

Sammanfattning: User authentication is vital in a secure system. Authentication is achieved through something a genuine user knows, has, or is. The latter is called biometrics, commonly attributed with fingerprint and face modalities. It is also possible to identify a user based on their behavior, called behavioral biometrics. In this study, keyboard and mouse behavior were considered. Previous research indicate promise for this authentication method. The research however is scarce, old and often not comprehensive. This study focus on two available data sets, the CMU keystroke dynamics dataset and the ReMouse data set. The data was used together with a comprehensive set of multi-class supervised classification machine learning algorithms from the scikit-learn library for Python. By performing hyperparameter optimization, two optimal algorithms with modified hyperparameters were found that improved results compared with previous research. For keystroke dynamics a classifier based on a neural network, multi-layer perceptron, achieved an Equal Error Rate (EER) of 1.26%. For mouse dynamics, a decision tree classifier achieved an EER of 0.43%. The findings indicate that the produced biometric classifiers can be used in an authentication model and importantly to strengthen existing authentication models such as password based login as a safe alternative to traditional Multi-Factor Authentication (MFA).