JBCA: Designing an Adaptative Continuous Authentication Architecture

Several continuous authentication methods provide very accurate ways of detecting the impersonation of a user . However, even though they should be access control chain’s links, they are commonly documented as algorithms unrelated to any system. In this paper, we propose a continuous authentication architecture to integrate several kinds of continuous authentication engines in a complete framework. The architecture covers different use cases where continuous authentication methods, placed in the user’s device or in the cloud, collaborate to determine if the user’s identity has changed. We use ensemble learning principles to combine the values generated by each CA model, and compute the trust level adaptively, addressing common errors caused by changes in the environments of continuous authentication systems.