We describe the use of keystroke-dynamics patterns for authentication and detecting infected hosts, and evaluate its robustness against forgery attacks. Specifically, we present a remote authentication framework called TUBA for monitoring a user's typing patterns. We evaluate the robustness of TUBA through comprehensive experimental evaluation including two series of simulated bots. Support vector machine is used for classification. Our results based on 20 users' keystroke data are reported. Our work shows that keystroke dynamics is robust against synthetic forgery attacks studied, where attacker draws statistical samples from a pool of available keystroke datasets other than the target. TUBA is particularly suitable for detecting extrusion in organizations and protecting the integrity of hosts in collaborative environments, as well as authentication.