Fuser block technologies performance based on identity attributes metrics models

The identity of people and entities has continued to be a central theme in both the real and the cyber space. In this paper, we compare three fuser block technologies used in a multifactor authentication system based on two identity attributes metrics models. The fuser blocks are built using artificial intelligence technologies which include Artificial Neural Networks (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The identity attributes metrics model is developed using term weight from text mining techniques and entropy from information theory. The actual experimental prototype is developed using four authentication factors. The results show that ANFIS was not affected by the type of identity attribute metric model used while ANN showed a better performance when using entropy metric system and FIS showed a better performance when using term weight metric system. These results are significant when building robust web based authentication systems that combine biometrics, Internet terminals and other authentication factors such as the username and password/PIN number.

[1]  Hamzeh Khazaei,et al.  Fingerprint Matching Algorithm Based on Voronoi Diagram , 2008, 2008 International Conference on Computational Sciences and Its Applications.

[2]  Julian Fierrez.  Adapted fusion schemes for multimodal biometric authentication (Esquemas adaptados de fusión para autenticación biométrica multimodal) , 2006 .

[3]  Marit Hansen,et al.  Identity management throughout one's whole life , 2008, Inf. Secur. Tech. Rep..

[4]  Fadi N. Sibai,et al.  Iris recognition using artificial neural networks , 2011, Expert Syst. Appl..

[5]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[6]  Abdel Nasser H. Zaied,et al.  Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait , 2011, Expert Syst. Appl..

[7]  Theodora Varvarigou,et al.  Identity management in GRID computing and Service Oriented Architectures: research and practice , 2009 .

[8]  Banafshe Arbab-Zavar,et al.  On guided model-based analysis for ear biometrics , 2011, Comput. Vis. Image Underst..

[9]  Cornelia M. Ruland,et al.  Secure solution for mobile access to patient's health care record , 2011, 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services.

[10]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[11]  Xiao-qiang Zhang,et al.  Neural Network Based Algorithm and Simulation of Information Fusion in the Coal Mine , 2007 .

[12]  Madasu Hanmandlu,et al.  Score level fusion of multimodal biometrics using triangular norms , 2011, Pattern Recognit. Lett..

[13]  Yinan Yang,et al.  Profile-based digital identity management — a better way to combat fraud , 2010, 2010 IEEE International Symposium on Technology and Society.

[14]  Sebastian Möller,et al.  Identity theft, computers and behavioral biometrics , 2009, 2009 IEEE International Conference on Intelligence and Security Informatics.

[15]  L. Anthony,et al.  AntConc: design and development of a freeware corpus analysis toolkit for the technical writing classroom , 2005, IPCC 2005. Proceedings. International Professional Communication Conference, 2005..

[16]  Suziah Sulaiman,et al.  User requirement model for federated identities threats , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[17]  Gang Liu,et al.  Antiphishing through Phishing Target Discovery , 2012, IEEE Internet Computing.

[18]  Yi Pan,et al.  A Reliable Metric for Quantifying Multiple Sequence Alignment , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[19]  Tiejun Zhao,et al.  Identity attributes quantitative analysis and the development of a metrics model using text mining techniques and information theory , 2010, 2010 IEEE International Conference on Information Theory and Information Security.

[20]  Patrick J. Flynn,et al.  Genetically identical irises have texture similarity that is not detected by iris biometrics , 2011, Comput. Vis. Image Underst..

[21]  Michael Hamelin.  Preparing for a firewall audit , 2011, Netw. Secur..