This paper uses a fuzzy logic approach in a static typing biometrics user authentication. The inputs are the down and up times, and the ASCII code of the keys that are captured while the user is typing a known string. In this research, it was collected four features (the key code, two keystroke latencies and the key duration) captured in two different strings. The first string was imposed, and the second one was chosen by each user. Seven experiments were developed utilizing a fuzzy logic classifier and the proposed features. The results of the experiments are evaluated in three situations of authentication: the legitimate user, the impostor and the specialist impostor. The best results were achieved utilizing all the features, obtaining a false rejection rate of 3.5% and a false acceptance rate of 2.9%. This approach can be used in the usual login-password authentication for improvement of the false acceptance rate, when the password is no more a secret.In this paper recurrent neural networks are considered to realize traffic prediction in computer network.