Towards a masquerade detection system based on user's tasks
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Nowadays, computers store critical information, prompting the development of mechanisms aimed to timely detect any kind of intrusion. Some of such mechanisms, called masquerade detectors, are often designed to signal an alarm whenever they detect an anomaly in system behavior. Usually, the profile of ordinary system behavior is built out of a history of command execution. However, in [1,2], we suggested that it is not a command, but the object upon which it is carried out what may distinguish a masquerade from user participation; also, we hypothesized that this approach provides a means for building masquerade detectors that work at a higher-level of abstraction. In this paper, we report on a successful step towards this hypothesis validation. The crux of our abstraction stems from that a directory often holds closely related objects, resembling a user task; thus, we do not have to account for the accesses to individual objects; instead, we simply take it to be an access to some ancestor directory of it, the user task. Indeed, we shall prove that by looking into the access to only a few such user tasks, we can build a masquerade detector, just as powerful as if we looked into the access to every single file system object. The advantages of this abstraction are paramount: it eases the construction and maintenance of a masquerade detection mechanism, as it yields much shorter models. Using the WUIL dataset , we have conducted two experiments for distinguishing the performance of two one-class classifiers, namely: Naïve Bayes and Markov chains, considering single objects and our abstraction to user tasks. We shall see that in both cases, the task-based masquerader detector outperforms the individual object-based one. © 2014 Springer International Publishing.