Behavioral Biometrics Authentication Systems: Leveraging Machine Learning for Enhanced Cybersecurity

This study focuses on behavioral biometrics and machine learning procedure that is employed vigorous in the verification frameworks to improve cybersecurity. Elaborating interesting designs in client behaviour, such as keystroke flow and mouse developments along with advanced machine learning algorithms we propose to generate verification components capable of accurately identifying a person's identity while adapting themselves all the fast live changes associated either. The experiments carried out on real-life datasets demonstrate LSTM models pervasive implementation in contrast to traditional machine learning algorithms (SVM, KNN and Random Forest). Thus, LSTM achieved an accuracy of 95.6%, outperforming SVM (88.7%), KNN (84.5%) and Random Forest (923). Additionally, LSTM proved great flexibility and competitive performance despite longer training times. The abovementioned results have demonstrated that LSTM is effective in learning the complicated real world conditions in sequential data and could be used for improvement of behavioral biometric recognition systems.