Enhanced recognition of keystroke dynamics using Gaussian mixture models

@article{eker2015EnhancedRO,
  title={Enhanced recognition of keystroke dynamics using Gaussian mixture models},
  author={Hayreddin Çeker and Shambhu J. Upadhyaya},
  journal={MILCOM 2015 - 2015 IEEE Military Communications Conference},
  year={2015},
  pages={1305-1310}
}
Keystroke dynamics is a form of behavioral biometrics that can be used for continuous authentication of computer users. Many classifiers have been proposed for the analysis of acquired user patterns and verification of users at computer terminals. The underlying machine learning methods that use Gaussian density estimator for outlier detection typically assume that the digraph patterns in keystroke data are generated from a single Gaussian distribution. In this paper, we relax this assumption… CONTINUE READING

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Key Quantitative Results

  • Out of 30 users with dynamic text, we obtain 0.08% Equal Error Rate (EER) with 2 components by using GMM, while pure Gaussian yields 1.3% EER for the same data set (an improvement of EER by 93.8%).

References

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Keystroke dynamics user authentication based on gaussian mixture model and deep belief nets

  • Y. Deng, Y. Zhong
  • International Scholarly Research Notices
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2 Excerpts

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