The interaction between humans and most desktop and laptop computers is often performed through two input devices: the keyboard and the mouse. Continuous tracking of these devices provides an opportunity to verify the identity of a user, based on a profile of behavioral biometrics from the user's previous interaction with these devices. We propose a bank of sensors, each feeding a binary detector (trying to distinguish the authentic user from all others). In this study the detectors use features derived from the keyboard and the mouse, and their decisions are fused to develop a global authentication decision. The binary classification of the individual features is developed using Naive Bayes Classifiers which play the role of local detectors in a parallel binary decision fusion architecture. The conclusion of each classifier (ï¿ï¿ï¿authentic userï¿ï ¿ï¿ or ï¿ï¿ï¿otherï ¿ï¿ï¿) is sent to a Decision Fusion Center (DFC) where we use the Neyman-Pearson criterion to maximize the probability of detection under an upper bound on the probability of false alarms.We compute the receiver operating characteristic (ROC) of the resulting detection scheme, and use the ROC to assess the contribution of each individual sensor to the quality of the global decision on user authenticity. In this manner we identify the characteristics (and local detectors) that are most significant to the development of correct user authentication. While the false accept rate (FAR) and false reject rate (FRR) are fixed for the local sensors, the fusion center provides trade-off between the two global error rates, and allows the designer to fix an operating point based on his/her tolerance level of false alarms. We test our approach on a real-world dataset collected from 10 office workers, who worked for a week in an office environment as we tracked their keyboard dynamics and mouse movements during interaction with laptops and desktop computers.