Associative Patterns of Web Browsing Behavior

As more people use the Web through a browser to gather and disseminate information, recognizing Web browsing signatures can complement other behavioral biometrics such as keystroke authentication to verify a claim of identity and/or identify persons of interest. The deluge of available digital traces enables the cognitive analysis of behavioral traits that differentiate between users and predict their online behavior. Recommendation systems have long capitalized on this capability to personalize search queries but have not exploited the temporal structure of preferences. This paper claims that spatio-temporal patterns of category of website visited by time of access can uniquely characterize and identify users. We present some exploratory approaches in user identification based on recurrent neural networks and empirical results based on clickstream data obtained through a user study and through an internet data provider.

[1]  David W. Aha,et al.  User Authentication from Web Browsing Behavior , 2013, FLAIRS.

[2]  Ravi Kumar,et al.  A characterization of online browsing behavior , 2010, WWW '10.

[3]  Malek Ben Salem,et al.  Detecting Masqueraders: A Comparison of One-Class Bag-of-Words User Behavior Modeling Techniques , 2010, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[4]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[5]  Christopher Krügel,et al.  A Practical Attack to De-anonymize Social Network Users , 2010, 2010 IEEE Symposium on Security and Privacy.

[6]  Shouhong Wang,et al.  Password Authentication Using Hopfield Neural Networks , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  L. Sweeney.  Replacing personally-identifying information in medical records, the Scrub system. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[8]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[9]  Koen W. De Bock,et al.  Predicting Website Audience Demographics forWeb Advertising Targeting Using Multi-Website Clickstream Data , 2010, Fundam. Informaticae.

[10]  K. Fernow.  New York , 1896, American Potato Journal.

[11]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

[12]  Dawn Xiaodong Song,et al.  On the Feasibility of Internet-Scale Author Identification , 2012, 2012 IEEE Symposium on Security and Privacy.

[13]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Alex Pentland,et al.  Honest Signals - How They Shape Our World , 2008 .

[15]  Douglas M. Wood,et al.  Alone Together, Why We Expect More from Technology and Less from Each Other , 2012 .

[16]  Thorsten Joachims,et al.  WebWatcher : A Learning Apprentice for the World Wide Web , 1995 .

[17]  Sherry Turkle,et al.  Alone Together: Why We Expect More from Technology and Less from Each Other , 2011 .

[18]  Albrecht Schmidt,et al.  Knowing the User's Every Move – User Activity Tracking for Website Usability Evaluation and Implicit Interaction , 2006 .

[19]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[20]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[21]  A. Beygelzimer.  Multiclass Classification with Filter Trees , 2007 .

[22]  Balaji Padmanabhan,et al.  Clickprints on the Web: Are There Signatures in Web Browsing Data? , 2007 .