Bolstering user authentication

In most information systems today, static user authentication is accomplished when the provides a credential (for example, ID and matching password). However, passwords appear to be insecure method as they are vulnerable attacks chiefly caused by poor password hygiene. We contend that an additional, non-intrusive level of security can achieved analyzing keystroke biometrics coming up with unique biometric template user's typing pattern. The paper proposes new model for representing raw data collected biometrics. based on fuzzy sets kernel functions. corresponding algorithm developed. problem, our demonstrated relatively higher performance than some classic anomaly-detection algorithms, such Mahalanobis, Manhattan, nearest neighbor, outlier counting, neural network, support-vector machine.