A novel security scheme for behavioral authentication systems based on keystroke dynamics

Abstract

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.

A privacy‐preserving multifactor authentication system

“…Physiological biometrics, such as fingerprints, iris, and face, have already drawn considerable attention in academia and have been implemented widely in industry . Behavioral biometrics, such as mouse movements, keystroke dynamics, graphical passwords, though not widely utilized, have also gained popularity in the research community . Similar to MFA systems with possession factors , MFA systems with physiological biometrics suffer from relatively low usability and deployability due to the implementation cost of biometrics recognition devices.…”

Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

“…Over the last six decades, many classification methods have been applied in keystroke dynamics study. Keystroke dynamics can be perceived as a pattern recognition problem, and the most commonly deployed methods can be broadly categorized as statistical and machine learning approaches [12].…”

“…[11]. Authors also focus on comprehensive analysis using KSD using Neural network; different neural network classifiers are used in this work in order to find different performance parameters like false acceptance rate, low error rate, and equal error rate [12].…”

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

“…[15]. Moreover, in [16] researchers had combined the timing features with non-timing features i.e., pressure, position, finger placement and finger choice for tying behaviour analysis.…”