Аннотация
This article presents a comprehensive analysis behavioral authentication systems based on keystroke dynamics using android mobile devices. Behavioral security authentication is an efficient biometric‐based security system that can be used to authenticate users. It is exploited to strengthen password authentication efficiently and inexpensively because no extra hardware is required in most of these schemes. Keystroke dynamic rhythm uses combinations of timing features and nontiming features that are extracted and processed from several devices such as classical keyboard and built‐in software keyboard in touch screen and smart phone devices. This work presents a comprehensive analysis of using biometric behavioral authentication system‐based on keystroke dynamics. Neural network classifiers are used in this work. The performance results show that keystroke dynamics provides good level in performance measures as a second authentication factor. The distinguishable role for nontiming features in addition to the timing features is demonstrated. These features have a significant role in improving the performance measures. The proposed model achieves low error rate of 0.3% for false acceptance rate, a false rejection of 1.5%, and an equal error rate of 0.9%. These are considered excellent enhancements when compared with previous reported results. Suggestions for future directions, and challenges for using behavioral‐based authentication systems are also highlighted.