Keystroke Dynamics in Mobile Platform

Abstract

Recently popularity in current digital devices with touch sense system such as smartphones, iPad, iPod, Nintendo DS, Automated Teller Machine (ATM), Windows 10 devices and etc. Smartphone is the one of main communication device in current global because it is portable with-it size and ease to carry. Other than that, it has numerous features combine into one device. User can do their transaction through online banking with their phone for online shopping, fund transfer, pay bills and view current balance using smartphone. A smartphone is become most important device in daily life so the smartphone security is an important issue. To secure the sensitive data for stored and accessed from, smartphone device has made user authentication become an importance major issue. Most of the smartphone devices using traditional way such as setting pin and password. Pin is using 4 random numerical number 1 to 9 while Password using 4 to 32 numerical number 1 to 9. A different security lock in smartphone is pattern lock while user can draw different pattern into the 9 dots to lock the phone. While for current security using biometric fingerprint security for unlock phone. It was dominant to analyze users' daily behaviors and evolve mobile phones into truly intelligent personal devices to secure users. While making the device truly intelligent should implement machine learning with biometric together and let the device learn and predict future. However, keystroke dynamics is the one of the behavior biometric that can implement with machine learning to create continuously authentication to track the owner of the devices.