Adaptability of current keystroke and mouse behavioral biometric systems: A survey

Research in behavioral biometrics, especially keystroke and mouse behavioral biometrics, has increased in recent years, gaining traction in industry and academia across various fields, including the detection of emotion, age, gender, fatigue, identity theft, and online assessment fraud. These methods are popular because they collect data non-invasively and continuously authenticate users by analyzing unique keystroke or mouse behavior. However, user behavior evolves over time due to several underlying factors. This can affect the performance of current keystroke and mouse behavioral biometric-based user authentication systems. We comprehensively survey current keystroke and mouse behavioral biometric approaches, exploring their use in user authentication and other real-world applications while outlining trends and research gaps. In particular, we investigate whether current approaches compensate for user behavior evolution. We find that current keystroke and mouse behavioral biometrics approaches cannot adapt to user behavior evolution and suffer from limited efficacy. Our survey highlights the need for new and improved keystroke and mouse behavioral biometrics approaches that can adapt to user behavior evolution. This study will assist researchers in improving current research efforts toward developing more secure, effective, sustainable, robust, adaptable, and privacy-preserving keystroke and mouse-behavioral biometric-based authentication systems.