Knowledge-based user authentication technique is a common and easy access control mechanism. But people are uninspired while choosing a healthy password or PIN. It increases the probability of guessing attacks. In this situation, to minimize these attacks, keystroke dynamics is a good choice; here users are not only identified by the password but their typing style is also accounted for. Keystroke dynamics is the method of analyzing typing pattern on a computer keyboard or touch screen and classifying the users based on their regular typing rhythm. It is a behavioral biometric characteristic which we have learned in our life and relates to the issues in human identification/authentication. This is the method where people can be identified by their typing style similar to hand writing or voice print. Being noninvasive and cost-effective, this method is a good field of research. But the performance of keystroke dynamics is less than other popular morphological biometric characteristics like face print, iris, and finger print recognition due to high rate of intraclass variation or high Failure to Enroll Rate (FER). So, this technique demands higher level of security. In this chapter, we are interested in investigating the integration of the soft biometric features, gender and age group, with the existing keystroke dynamics user authentication systems proposed by [1-3]. © 2018 by Taylor & Francis Group, LLC.