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Human Behavior-Based Keystroke Password Authentication: A Behavioral Biometrics Approach
Abstract
This article presents the development of a keystroke password authentication system based on the uniqueness of human typing behavior as behavioral biometrics. It analyzes keystroke dynamics, including timing, speed, and rhythm, providing a unique and secure method for user authentication. Traditional password-based systems are vulnerable to attacks such as phishing and brute force. Keystroke-based systems require the correct typing pattern along with the password, adding an extra security layer. This paper reviews existing methods, noting their strengths and limitations, and proposes a model using advanced machine learning algorithms to enhance accuracy and reliability. The proposed keystroke dynamics method demonstrates high accuracy and low false positive rates, proving its effectiveness in preventing unauthorized access. The results emphasize its potential as a reliable solution for secure authentication in modern network security frameworks.
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