A Novel and Simple Statistical Fusion Method for User Authentication Through Keystroke Features
In this paper, the authors utilize a statistical fusion method to extract the characteristic information for Keystroke-based Authentication (KA) systems to verify users' identities. The keystroke data is based on the time instances of pressing and releasing a key, and five features based on the time periods are calculated using these data. In the experiment, nineteen users participated as the legitimate users and each account was attacked by between 62 and 82 impostors. The average false acceptance rate is 1.035% and the false rejection rate is 0%. These rates are both competitive with other researches. The proposed method can be used as the classifier in password-based authentication systems to enhance their security.