Mini-batch bagging and attribute ranking for accurate user authentication in keystroke dynamics

We consider the problem of differentiating users' typing behavior patterns using machine learning algorithms with keystroke dynamics features. We have proposed mini-batch bagging (MINIBAG) method and attribute ranking of one-class naïve Bayes (AR-ONENB) algorithm. MINIBAG is motivated from bagging because MINIBAG chunks each attribute of the dataset into multiple sub-datasets during the preprocessing phase. Meanwhile, AR-ONENB sorts the attributes based on the time length during the preprocessing phase for effective classification. Both proposed algorithms have shown promising experimental results from various keystroke dynamics based user authentication benchmark tests. From the experimental results, it can be seen that MINIBAG facilitates machine learning algorithms to have an ensemble of multiple models from mini-batches. AR-ONENB, on the other hands, calculates log-likelihood value from keystroke index order for anomaly estimation, which exploits the observation that the user's typing speed is unique.