Individuals all throughout the world are concerned about their safety and privacy. Our digital devices continue to be vulnerable to data loss, emphasising the need for improved data and system safety via advanced authentication systems. Behavioural biometric techniques provide a solution by allowing continuous monitoring and surveillance of a person’s behaviours. Touch-based behavioural biometrics and mouse dynamics are examples of these techniques. This study provides an in-depth examination of the various strategies used by researchers in the field of behavioural biometric authentication to protect data and devices. Our survey categories these strategies based on the deep learning or IoT based frameworks used, the feature extraction procedure, and the datasets accessible for experimentation. Furthermore, we provide an overview of the attained performance levels, the implementation obstacles, and prospective future opportunities in this subject.