Anomaly User Detection via Comprehensive Keystroke Features Optimization

@article{Li2018AnomalyUD,
  title={Anomaly User Detection via Comprehensive Keystroke Features Optimization},
  author={Meng Li and Bin Wu and Zhengcai Qin},
  journal={2018 International Joint Conference on Neural Networks (IJCNN)},
  year={2018},
  pages={1-7}
}
This paper aims at the problem of anomaly user detection, in which a novel, effective and comprehensive feature extraction method is proposed. Instead of existing keystroke timing information from the dataset, three types of new features are extracted in our approach for a better description of user keystroke characteristics. Moreover, an AdaBoost based algorithm is used to generate an optimized anomaly user detection model based on comprehensive keystroke features. This model contains the… CONTINUE READING

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