Title
Toward a Framework for Continuous Authentication Using Stylometry
Abstract
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 features and develop a new authorship verification model applicable for continuous authentication. We use existing lexical, syntactic, and application specific features, and propose new features based on n-gram analysis. We start initially with a large features set, and identify a reduced number of user-specific features by computing the information gain. In addition, our approach includes a strategy to circumvent issues regarding unbalanced dataset which is an inherent problem in stylometry analysis. We use Support Vector Machine (SVM) for classification. Experimental evaluation based on the Enron email dataset involving 76 authors yields very promising results consisting of an Equal Error Rate (EER) of 12.42% for message blocks of 500 characters.
Year
DOI
Venue
2014
10.1109/AINA.2014.18
Advanced Information Networking and Applications
Keywords
Field
DocType
authorisation,document handling,support vector machines,CA,EER,Enron email dataset,SVM,authorship verification model,computing session,continuous authentication,equal error rate,impostor behaviors,legitimate behaviors,n-gram analysis,stylometry analysis,support vector machine,target document,user-specific features,Continuous authentication,authorship verification,biometrics systems,classification,n-gram features,security,short message verification,stylometry,text mining,writeprint
Data mining,Authentication,Computer science,Stylometry,Artificial intelligence,Syntax,Distributed computing,Writeprint,Support vector machine,Word error rate,Feature extraction,Authorship verification,Machine learning
Conference
ISSN
Citations 
PageRank 
1550-445X
6
0.44
References 
Authors
26
3
Name
Order
Citations
PageRank
Marcelo Luiz Brocardo1304.04
Issa Traore230632.31
Isaac Woungang346179.73