Abstract
Keystroke dynamics, an automated method and promising biometric technique, is used to recognize an individual, based on an analysis of user’s typing patterns. The processing steps involved in keystroke dynamics are data collection, feature extraction and feature selection. Initially the statistical measures of feature characteristics like latency, duration and digraph are computed during feature extraction. Various advanced optimization techniques are applied by researchers to mimic the behavioral pattern of key stroke dynamics. In this study, Firefly algorithm (FA) is proposed for feature selection. The performance efficiency of FA is computed and compared with existing techniques and found that the convergence rate and iteration generations to reach the optimum solution is 41% and 18% less respectively, as compared to those by other algorithms.
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Muthuramalingam, A., Gnanamanickam, J., Muhammad, R. (2018). Optimum Feature Selection Using Firefly Algorithm for Keystroke Dynamics. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_39
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DOI: https://doi.org/10.1007/978-3-319-76348-4_39
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