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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Saturday 20 October 2018

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Improving the Performance of Mouse Dynamics Based Authentication Using Machine Learning Algorithm


Volume 24, Issue 1, June 2016, Pages 202–209

 Improving the Performance of Mouse Dynamics Based Authentication Using Machine Learning Algorithm

S. Suganya, G. Muthumari, and C. Balasubramanian

Original language: English

Received 14 March 2016

Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


To diminish the behavioral variability of mouse dynamics, the machine learning algorithm was proposed. Mouse dynamics is the process of identifying the user based on their mouse operating behavior. The dataset includes co-ordinates values, time stamp value and mouse operation. From this dataset, the schematic features, holistic features and motor-skill features like average speed, average distance, mean, standard deviation and mouse silence ratio, velocity, slope angle, curvature were extracted to obtain feature vector. The obtained feature vector can be applied to the dimensionality reduction based approach, diffusion map to reduce the dimension of the feature vector that compared with ISOMAP (Isometric Feature Mapping). Without dimensionality reduction based method the classification process was difficult. The machine learning algorithm i.e.) hop field network to be used to identify whether the given input sample was authenticated user (or) unauthenticated.

Author Keywords: behavioral variability, mouse dynamics, feature vector, dimensionality reduction, diffusion map, hop field network.


How to Cite this Article


S. Suganya, G. Muthumari, and C. Balasubramanian, “Improving the Performance of Mouse Dynamics Based Authentication Using Machine Learning Algorithm,” International Journal of Innovation and Scientific Research, vol. 24, no. 1, pp. 202–209, June 2016.