Continuous authentication using micro-messages

Authorship verification consists of checking whether a target document was written or not by a specific individual. In this paper, we study the problem of authorship verification for Continuous Authentication (CA) purposes. Different from traditional authorship verification that focuses on long texts, we tackle the use of micro-messages. Shorter authentication delay (i.e. smaller data sample) is essential to reduce the window size of the re-authentication period in CA. We explored lexical, syntactic, and application specific features. We investigated two different classification schemes: on one hand Logistic Regression (LR) and on the other hand an hybrid classifier combining Support Vector Machine (SVM) and LR. Experimental evaluation based on the Enron email dataset involving 76 authors and Twitter dataset involving 100 authors yield very promising results consisting of Equal Error Rates (EER) of 9.18% and 11.83%, respectively.