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上传时间：2017-10-23
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[主讲人: Mohammad S. Obaidat教授（IEEE Fellow, SCS Fellow,美国蒙莫] [时间: 2012-11-27 00:00:00]
Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed. In this talk, we propose a new online biometric risk-based authentication system that provides more robust user identity information by combining mouse dynamics and keystroke dynamics biometrics in a multimodal framework. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 8.21%, which is promising considering that we are dealing with free text and free mouse movements, and the fact that many web sessions tend to be very short. Moreover, we believe this performance is adequate for reactive risk-based authentication, where the goal is not to prevent the user from using the system, but rather to identify malicious sessions and trigger appropriate risk mitigation measures.
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