One-Class Training for Masquerade Detection (2003)
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| Venue: | 3rd IEEE Conference Data Mining Workshop on Data Mining for Computer Security |
| Citations: | 11 - 3 self |
BibTeX
@INPROCEEDINGS{Wang03one-classtraining,
author = {Ke Wang and Salvatore J. Stolfo},
title = {One-Class Training for Masquerade Detection},
booktitle = {3rd IEEE Conference Data Mining Workshop on Data Mining for Computer Security},
year = {2003}
}
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Abstract
We extend prior research on masquerade detection using UNIX commands issued by users as the audit source. Previous studies using multi-class training requires gathering data from multiple users to train specific profiles of self and non-self for each user. Oneclass training uses data representative of only one user. We apply one-class Naïve Bayes using both the multivariate Bernoulli model and the Multinomial model, and the one-class SVM algorithm. The result shows that oneclass training for this task works as well as multi-class training, with the great practical advantages of collecting much less data and more efficient training. One-class SVM using binary features performs best among the oneclass training algorithms. 1.
