Jul 12, 2018 · In this paper, we propose a novel classification ensemble for file access-based masquerade detection. We have successfully validated the ...

This paper has successfully validated the hypothesis that a one-class classification approach to file access-based masquerade detection outperforms a ...

Oct 22, 2024 · In this paper, we propose a novel classification ensemble for file access-based masquerade detection. We have successfully validated the ...

Title: Bagging-RandomMiner: a one-class classifier for file access-based masquerade detection. ; Language: English ; Authors: Camiña, José Benito1 (AUTHOR) ...

Bagging-RandomMiner: a one-class classifier for file access-based masquerade detection. https://doi.org/10.1007/s00138-018-0957-4. Journal: Machine Vision and ...

We have successfully validated the hypothesis that a one-class classification approach to file access-based masquerade detection outperforms a multi-class one.

BaggingRandomMiner is an ensemble of weak one-class classifiers based on dissimilarities: BRM can work with only numerical features.

BaggingRandomMiner [1] is a model based on the methodology of ensembles for the classification of a single class, which bases its training on randomization.

Bagging-RandomMiner is an instance-based-learning algorithm for one-class classification [1]. ... file access-based masquerade detection," Machine Vision and ...

Co-authors ; Bagging-RandomMiner: A one-class classifier for file access-based masquerade detection. JB Camiña, MA Medina-Pérez, R Monroy, O Loyola-González, ...