Table of Contents
ISRN Signal Processing
Volume 2013, Article ID 565183, 7 pages
Research Article

Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets

6 New England Executive Park, Burlington, MA 01803, USA

Received 30 June 2013; Accepted 18 August 2013

Academic Editors: S. Kwong and K. Wang

Copyright © 2013 Yunbin Deng and Yu Zhong. 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.


User authentication using keystroke dynamics offers many advances in the domain of cyber security, including no extra hardware cost, continuous monitoring, and nonintrusiveness. Many algorithms have been proposed in the literature. Here, we introduce two new algorithms to the domain: the Gaussian mixture model with the universal background model (GMM-UBM) and the deep belief nets (DBN). Unlike most existing approaches, which only use genuine users’ data at training time, these two generative model-based approaches leverage data from background users to enhance the model’s discriminative capability without seeing the imposter’s data at training time. These two new algorithms make no assumption about the underlying probability distribution and are fast for training and testing. They can also be extended to free text use cases. Evaluations on the CMU keystroke dynamics benchmark dataset show over 58% reduction in the equal error rate over the best published approaches.