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

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Provided by: Hindawi Publishing
Topic: Security
Format: PDF
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, the authors 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.
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