|dc.description.abstract||Most of the current computer systems authenticate the user identity only at the point of entry to the system (i.e., login). However, an effective authentication system includes continuous or frequent monitoring of the identity of the user to ensure the valid identity of the user throughout a session. Such a system is called a continuous authentication system. An authentication system with such security scheme protect against certain attacks such as session hijacking that can be performed by a malicious user.
Recently, keystroke analysis has acquired popularity as one of the main approaches in behavioral biometrics techniques that can be used for continuously authenticating user. There are several advantages when applying keystroke analysis: First, keystroke dynamics are practical, since every user of a computer types on a keyboard. Second, keystroke analysis is inexpensive because it does not require any additional components (such as special video cameras) to sample the corresponding biometric feature. Third and most importantly, typing rhythms can be still available even after the authentication stage has been passed.
A major challenge in keystroke analysis is the identification of the major factors that influence the performance accuracy of the keystroke authentication detector. Two of the most influential factors that may impact the performance accuracy of the keystroke authentication detector include the classifier employed and the choice of features.
Currently, there is insufficient research that addresses the impact of these factors in continuous authentication analysis. The majority of exciting studies in keystroke analysis focuses primarily on the impact of these factors in the static authentication analysis. Understanding the impact of these factors will contribute to the improvement of continuous authentication keystroke based system performance. Furthermore, most of the existing schemes of keystroke analysis require having predefined typing models either for legitimate users or impostors. However, it is difficult or even impossible in some situations to have typing data of the users (legitimate or impostors) in advance. For instance, consider a personal computer that a user carries to a college or to a cafe. In this case, only the computer owner (legitimate user) is known in advance. For another instance, consider a computer that has a guest account in a public library; in this case, none of the system users are known in advance. Thus, a new automated and flexible technique that has the ability to authenticate the user without the need for any prior user typing model is needed.
This dissertation focuses on improving continuous user authentication systems (that are based on keystroke dynamics) designed to detect malicious activity caused by another person (impostor) whose goals to is take over the active session of a valid user. The research will be carried out by1) studying the impact of the selected features on the performance of keystroke continuous authentication systems; 2) proposing new timing features that based on utilization of the most frequently used English words (e.g. “The”, “And”, For””) that can be useful in distinguishing between users in continuous authentication systems; 3) comparing the performance of keystroke continuous authentication systems with the application of different algorithms; 4) investigating the possibility of improving the accuracy of continuous user authentication systems by combining more than one feature; 5) proposing a new detector that does not require predefined typing models either from legitimate users or impostors.||