Cognitive-Based Biometrics System for Static User Authentication

In today´s globally expanding business world, protecting the identity and transactions of online consumers is crucial for any company to reach out for new markets. This directs digital information technologies towards the adoption of stronger and more secure authentication schemes. Although biometric-based user authentication systems have proven superiority over the traditional ones, there are several barriers for their wide scale deployment and application for Internet security; barriers include high expensive equipment, and low precision sensor technologies. In this paper, we propose a novel biometric system for static user authentication. It introduces two new cognitive factors, namely visual scan & detection, and short-term memory. These two factors are homogeneously combined with mouse dynamics in one biometric system. Experimental evaluation was performed using mass enrollment of 275 participants, and neural network for classification. Results showed an equal error rate (EER) of 3.88%. The promising achieved performance, in addition to the fact that standard mouse is the only data input device required, make this system ideal for static authentication on the Internet.