Bagging-RandomMiner: a one-class classifier for file access-based masquerade detection

Abstract

Dependence on personal computers has required the development of security mechanisms to protect the information stored in these devices. There have been different approaches to profile user behavior to protect information from a masquerade attack; one such recent approach is based on user file-access patterns. In this paper, we propose a novel classification ensemble for file access-based masquerade detection. We have successfully validated the hypothesis that a one-class classification approach to file access-based masquerade detection outperforms a multi-class one. In particular, our proposed one-class classifier significantly outperforms several state-of-the-art multi-class classifiers. Our results indicate that one-class classification attains better classification results, even when unknown attacks arise. Additionally, we introduce three new repositories of datasets for the identification of the three main types of attacks reported in the literature, where each training dataset contains no object belonging to the type of attack to be identified. These repositories can be used for testing future classifiers, simulating attacks carried out in a real scenario.