Improved Feature Engineering for Free-Text Keystroke Dynamics

Abstract

Free-text keystroke dynamics is a method of verifying users’ identity based on their unique pattern of typing a spontaneous text on a keyboard. When applied in remote systems, it can add an additional layer of security that can detect compromised accounts. Therefore, service providers can be more certain that remote systems accounts would not be compromised by malicious attackers. Free-text keystroke dynamics usually involve the extraction of n-graphs, which represent the latency between n consecutive events. These n-graphs are then integrated with one of the various existing machine learning algorithms. To the best of our knowledge, n-graphs are the most widely used feature engineering for free text keystroke dynamics. We present extended-n-graphs, an improved version of the commonly used n-graphs, based on several extended metrics that outperform the traditionally used basic n-graphs. Our technique was evaluated on top of the gradient boosting algorithm, best performing algorithm on basic n-graphs and several additional algorithms such as random forest, K-NN, SVM and MLP. Our empirical results show encouraging 4% improvement in the Area Under the Curve (AUC) when evaluated on a publicly used benchmark.