A recently released market research report shows the market for machine learning growing at a rapid 44.1% compounded annual growth rate over the next five years, driven largely by the financial services sector, where big data can yield critical and actionable business insights. In the world of behavioral biometrics, machine learning, deep learning and artificial intelligence are all hand-in-glove. Behavioral biometrics identifies people by how they interact with devices and online applications. As opposed to something that someone has like a device, token or a static attribute like a fingerprint or a name, behavioral biometrics is a dynamic modality that is completely passive and works in the background, making it impossible to copy or steal. Today's behavioral biometric technologies can capture more than 2,000 parameters from a mobile device, including the way a person holds the phone, scrolls, toggles between fields, the pressure they use when they type and how they respond to different stimuli that are presented in online applications. Behavioral biometrics is used primarily for preventing the use of stolen or synthetic identities in applying for credit online and in preventing account takeovers once a user is logged into a session.

As insider hacks become more prevalent it is becoming more useful to identify valid users from the inside of a system rather than from the usual external entry points where exploits are used to gain entry. One of the main goals of this study was to ascertain how well Gradient Boosting could be used for prediction or, in this case, classification or identification of a specific user through the learning of HCI-based behavioral biometrics. If applicable, this procedure could be used to verify users after they have gained entry into a protected system using data that is as human-centric as other biometrics, but less invasive. For this study an Extreme Gradient Boosting algorithm was used for training and testing on a dataset containing keystroke dynamics information. This specific algorithm was chosen because the majority of current research utilizes mainstream methods such as KNN and SVM and the hypothesis of this study was centered on the potential applicability of ensemble related decision or model trees. The final predictive model produced an accuracy of 0.941 with a Kappa value of 0.942 demonstrating that HCI-based behavioral biometrics in the form of keystroke dynamics can be used to identify the users of a system.