Password Protection Application

Our study shows a new approach to strengthening device security in touch-based interface security upgrades, covering mobile phones (iOS and Android platforms) and possible applications in Automated Teller Machines (ATMs). The crux of the whole process was found in the algorithmic program that incorporates keystroke dynamics and Mobile sensors into user authentication. Therefore, users can use this proposed application to address some of the inherent flaws that exist in password systems in smartphone devices that are becoming increasingly vulnerable to security breaches in today’s digital environment. The innovation of this application relied on using keystroke dynamics as biometric authentication. The method focuses on keystroke typing speed and mobile sensor. In addition, the algorithm for our proposed application is constantly learning from the unique timing of each individual’s keystrokes, thus making it more personalized and adaptive regarding security. Our timing-centric approach was applied to various smartphone models, with implications for ATMs also taken into account.

The evaluation is based on the accuracy of keystroke timing and sensors axis. This yielded a significant enhancement in authentication accuracy, underscoring the practical value of our application in fortifying defenses against prevalent cyber threats. This article discusses machine learning approaches used with keystroke time and mobile sensors to examine how this helps develop mobile security programs and provides an extensive assessment of system effectiveness. The paper strongly answers growing concerns related to safety problems concerning touchable devices, especially when considering smartphone operating systems.