Web based Keystroke Dynamics Identity Verification using Neural Network

Web based Keystroke Dynamics Identity

Verification using Neural Network

　

Sungzoon Cho¹, Chigeun Han², Dae Hee Han³, Hyung-Il Kim⁴

^{Department of Industrial Engineering
Seoul National University
San 56-1, Shinrimdong, Seoul 151-742, Korea.
Tel: +82-2-880-6275, Fax: +82-2-889-8560, E-mail: zoon@snu.ac.kr
　
^{2 Department of Computer Engineering
Kyung Hee University
Sochonri, Kiheung, Yongin 449-701, Kyunggi, Korea
E-mail: cghan@nms.kyunghee.ac.kr
　
^{3 SK Telecommunications
Hwaamdong, Yoosunggu, Daejon, 305-348, Korea.
E-mail: handol@sktelecom.com
　
^{4 Department of Computer Engineering
Kyung Hee University
Sochonri, Kiheung, Yongin 449-701, Kyunggi, Korea.
　
ABSTRACT
Password typing is the most widely used identity verification method in World Wide Web based Electronic Commerce. Due to its simplicity, however, it is vulnerable to imposter attacks. Keystroke dynamics and password checking can be combined to result in a more secure verification system. We propose an autoassociator neural network that is trained with the timing vectors of the owner's keystroke dynamics and then used to discriminate between the owner and an imposter. An imposter typing the correct password can be detected with very high accuracy using the proposed approach. This approach can be effectively implemented by a Java applet and used in the World Wide Web.
　
Keyword : Identity Verification, Keystroke Dynamics, Autoassociative Multilayer Perceptron, World Wide Web, Java Applet}}}}