A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

In recent years, computing devices have become widely distributed, and the accumulated data from these devices are growing rapidly, especially as they are increasingly equipped with various sensors and RF communication capabilities. Data science, including machine learning technology, has contributed to the better handling the large amounts of data and feature selection techniques have been a useful strategy. As data amounts continue to grow, scaling features will became crucial in data science. In this paper, we propose a novel filter-based feature-selection method in the context of keystroke dynamics authentication. In particular, we propose a new feature-scoring method and apply it to keystroke-dynamics-based authentications. We implement keystroke-dynamics-based authentications multi-factored with PIN-based authentications and collect data from actual users' testing experiments. Then, we apply our feature-selection method and compare the performance with that when using all of the features and existing feature-selection methods. Our experimental results show that the classification performance by the proposed method is superior to those of the other methods by up to 21.8%. Moreover, our method provides security to other users' data sets, as the method utilizes only mean values from imposter data. Our feature-selection method contributes to improving the quality of keystroke dynamics authentications without user privacy issues. More generally, our method can also be applied to other data-mining data sets, such as IoT sensor data sets.