Keystroke dynamics is a behavioural biometric deployed as a software based method for the authentication and/or identification of a user requesting access to a secured computing facility. It relies on how a user types on the input device (here assumed to be a PC keyboard)-and makes the explicit assumption that there are typing characteristics that are unique to each individual. If these unique characteristics can be extracted-then they can be used, in conjunction with the login details to enhance the level of access security-over and above the possession of the login details alone. Most unique characteristics involve the extraction of keypress durations and multi-key latencies. These characteristics are extracted during an enrollment phase, where a user is requested to login into the computer system repeatedly. The unique characteristics then form a string of some length, proportional to the enrollment character content times the number of attributes extracted. In this study, the deployment of classical string matching features prevalent in the bioinformatics literature such as position specific scoring matrices (motifs) and multiple sequence alignments to provide a novel approach to user verification and identification within the context of keystroke dynamics based biometrics. This study provides quantitative information regarding the values of parameters such as attribute acceptance thresholds, the number of accepted attributes, and the effect of contiguity. In addition, this study examined the use of keystroke dynamics as a tool for user identification. The results in this study yield virtually 100% user authentication and identification within a single framework.