Enhanced user authentication through typing biometrics with artificial neural networks and k-nearest neighbor algorithm

The emergence of global network access has promoted increased chances of malicious attack and intrusion. Password authentication has been known as the most commonly safeguard measure against these intrusions. Common it is, but the security measures that it provides have always been questionable. Thus, it gives rise to the need for a more secure and reliable authentication method in accessing computer systems. This paper proposes the design and development of a real time enhanced password security system through typing biometrics. Typing biometrics deals with the analysis of the unique habitual typing rhythms of individuals. The paper depicts the use of time latency between keystrokes to create typing patterns for individuals. Time latencies are extracted and classified accordingly; they are then used to recognize authentic users and reject imposters. The performance of both artificial neural networks and k-nearest neighbors as possible classifiers for this purpose were studied.