Master Thesis

The precise identification of users in the network at different moments in time is a well known and difficult problem. Identifying users by their actions (and not their IP addresses) allows administrators to apply policy controls on users, to find intruders that are impersonating legitimate users, and to find anomalous user behaviors that could be due to malware infections. More importantly, the behavioral analysis of users actions raises important moral questions about the power to identify users in unknown networks. This thesis explores this question by trying to identify users by converting the user's behavior into user's profiles. These profiles are time-dependent and they have dozen of features.