Corpus ID: 29155233

Extreme Gradient Boosting and Behavioral Biometrics

@inproceedings{Manning2017ExtremeGB,
  title={Extreme Gradient Boosting and Behavioral Biometrics},
  author={Benjamin Manning},
  booktitle={AAAI},
  year={2017}
}
As insider hacks become more prevalent it is becoming more useful to identify valid users from the inside of a system rather than from the usual external entry points where exploits are used to gain entry. One of the main goals of this study was to ascertain how well Gradient Boosting could be used for prediction or, in this case, classification or identification of a specific user through the learning of HCIbased behavioral biometrics. If applicable, this procedure could be used to verify… Expand
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References

SHOWING 1-6 OF 6 REFERENCES
Comparing anomaly-detection algorithms for keystroke dynamics
TLDR
The objective is to collect a keystroke-dynamics data set, to develop a repeatable evaluation procedure, and to measure the performance of a range of detectors so that the results can be compared soundly. Expand
Stochastic gradient boosting
Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. TheExpand
Induction of Decision Trees
TLDR
This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, which is described in detail. Expand
Learning to rank using gradient descent
TLDR
RankNet is introduced, an implementation of these ideas using a neural network to model the underlying ranking function, and test results on toy data and on data from a commercial internet search engine are presented. Expand
Ensemble Methods in Machine Learning
TLDR
Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly. Expand
Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1-15)
  • 2000