Keystroke dynamics authentication method based on ensemble learning and dual parallel adaptive mechanism
Identity authentication refers to the process of confirming the identity of an operator in a computer system.Keystroke dynam-ics,as a low-cost and difficult to imitate method of identity authentication,has received widespread attention from many scholars.How-ever,existing methods often have drawbacks such as high false positive and false negative rates,and poor generalization ability.In re-sponse to the above issues,this article proposes a method that combines ensemble learning and adaptive update mechanism to improve the classification performance of the model while adapting to feature changes in new data.By comparing our method with other ad-vanced technologies using publicly available CMU datasets and universal evaluation metrics(EER),experiments show that our pro-posed quadratic ensemble learning method has excellent performance.After using a dual parallel adaptive update mechanism,it exhib-its reliable generalization ability,achieving an EER of 3.22％on the CMU dataset.The model performance is better than similar stud-ies under the same experimental conditions.
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