An analysis of Particle Swarm Optimization and Genetic Algorithm with respect to keystroke dynamics

Keystroke Dynamics is one of the well-known and comparatively inexpensive behavioural biometric technologies, used in identifying the authenticity of a user when the user is working on a keyboard. In the field of computer security, ease of access of data by the authorized users is a major area of consideration. In addition to this, the protection of personal data from unauthenticated users is also a major challenge. In an earlier paper, an emerging non-static biometric technique was examined that aims to identify users based on the scrutiny of their habitual rhythm patterns in typing. Some features relating to time and depression of keys such as the duration of typed keys, latency between two consecutive keystrokes, Digraph, and Tri-graph were taken into account thus making the authentication depend upon the extracted features. In addition to these time related features, Virtual Key Force (VKF) is added as an additional feature for authentication. In the subsequent paper, a unique technique for authentication is adopted in key stroke dynamics by extracting different features of the user's rhythm in typing a text in the keyboard. The features extracted in the PSO methodology adopted are from the emotions of the users while typing the text. The feature selection for the proposed method uses Particle Swarm Optimization (PSO) algorithm. Typing rhythms are the rawest form of data stemming from the interaction between users and computers. When properly sampled and analyzed, they may become a useful tool to ascertain personal identity. Unlike other access control systems based on biometric features, keystroke analysis has not led to techniques providing an acceptable level of accuracy. In this paper, we have made a comparative analysis of Particle Swarm Optimization and Genetic Algorithm with respect to Keystroke Dynamics.