A Hybrid Method for Keystroke Biometric User Identification

The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions for n...

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Bibliographic Details
Published in:Electronics (Basel) 2022-09, Vol.11 (17), p.2782
Main Authors: Ali, Md L., Thakur, Kutub, Obaidat, Muath A.
Format: Article
Language:English
Subjects:
Access control
Accuracy
Biometrics
Computational linguistics
Data security
Datasets
Feature extraction
Language processing
Machine learning
Markov chains
Methods
Multilayer perceptrons
Natural language interfaces
Neural networks
Parameter identification
Passwords
Physiology
Sensors
Statistical analysis
Statistical models
Support vector machines
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Summary:The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions for new data, resulting in faster classification of new data compared to the generative models. In an attempt to build an efficient model for keystroke biometric user identification, this study proposes a hybrid POHMM/SVM method taking advantage of both generative and discriminative models. The partially observable hidden Markov model (POHMM) is an extension of the hidden Markov model (HMM), which has shown promising performance in user verification and handling missing or infrequent data. On the other hand, the support vector machine (SVM) has been a widely used discriminative model in keystroke biometric systems for the last decade and achieved a higher accuracy rate for large data sets. In the proposed model, features are extracted using the POHMM model, and a one-class support vector machine is used as the anomaly detector. For user identification, the study examines POHMM parameters using five different discriminative classifiers: support vector machines, k-nearest neighbor, random forest, multilayer perceptron (MLP) neural network, and logistic regression. The best accuracy of 91.3% (mean 0.868, SD 0.132) is achieved by the proposed hybrid POHMM/SVM approach among all generative and discriminative models.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11172782