A framework for next generation user authentication

The remarkable growth in digital data is changing what and how the defense against the unknown will take place. Big data is a technical term used today to represent this massive growth of digital data that's being created from many sources. Organizations have turned their attentions to the deployment of Big Data analytics to gain valuable insights that benefit their businesses within protected and secure environments. Hence, network security protocols, especially authentication protocols, are being re-designed to protect and to deliver the real benefits of this data growth. Contrary to the traditional perspective, in which researchers are focusing on identifying users' identity to protect Big Data-based environments, we have an opposite perspective that the Big Data itself would be the fuel for the next generation authentication. In other word, the main goal of this work is to propose a new framework for user authentication that leverages Big Data analytics. The core idea of this framework is to find out unique patterns of the users' dynamic behaviors. The proposed framework comprised of three main components. Data Security-based Analytics (DSA); describing the best utilization of the high velocity data streams, which is capable for distinguishing data that has security/identification potentials. Human dynamics measure engine; that develops an ambitious transformation from the Big Data characteristics into the relevant human dynamics measures. Big data-driven authentication service; describes the required engines to design software as a service-based authentication model. Our investigation shows that this new approach will help create a highly distributed authentication model, minimizing the storage of secrets, and lesser secret management overhead.