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This paper explores the use of deep belief networks for authorship verification model applicable for continuous authentication (CA). The proposed approach uses Gaussian units in the visible layer to model real‐valued data on the basis of a Gaussian‐Bernoulli deep belief network. The lexical, syntactic, and application‐specific features are explored, leading to the proposal of a method to merge a pair...
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...
Continuous Authentication (CA) consists of monitoring and checking repeatedly and unobtrusively user behavior during a computing session in order to discriminate between legitimate and impostor behaviors. Stylometry analysis, which consists of checking whether a target document was written or not by a specific individual, could potentially be used for CA. In this work, we adapt existing stylometric...
Authorship verification can be checked using stylometric techniques through the analysis of linguistic styles and writing characteristics of the authors. Stylometry is a behavioral feature that a person exhibits during writing and can be extracted and used potentially to check the identity of the author of online documents. Although stylometric techniques can achieve high accuracy rates for long documents,...
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