Keystroke Dynamics Authentication mechanism using Euclidean distance (KDA-ED) for Mobile and Cloud Learning

Abstract

In this paper, we convey about Keystroke Dynamics Authentication mechanism via Euclidean distance (KDA-ED) apparent and outstanding selection for touch screen devices especially designed for mobile cloud learning mechanism. Most of the current key stroke dynamics authentication algorithms are designed with relatively innumerous parameters like key hold time, finger location, key pressure etc in order to amplify the exactness for user validation. But it may increase the risk in user verification as a few users’ mental stability may vary and thus influence key stroke dynamics parameters.  KDA-ED is implemented with not many parameters like key elapsed time and key intervals, which will be able to extract the user patterns traced perfectly. It eases risk in user verification as a result that can be personalized with no trouble on mobile devices. The results of our experiments will give you an idea in relation to the proposed user verification scheme with few parameters by consuming low processing capabilities and better performance than other schemes.

Key words- Mobile and cloud learning, Key stroke dynamics, Key elapsed time, Key interval time, User key stroke patterns, Threshold value , Euclidean distance