Static versus dynamic data information fusion analysis using DDDAS for cyber security trust

Information fusion includes signals, features, and decision-level analysis over various types of data including imagery, text, and cyber security detection. With the maturity of data processing, the explosion of big data, and the need for user acceptance; the Dynamic Data-Driven Application System (DDDAS) philosophy fosters insights into the usability of information systems solutions. In this paper, we exp lore a notion of an adaptive adjustment of secure communication trust analysis that seeks a balance between standard static solutions versus dynamic -data driven updates. A use case is provided in determining trust for a cyber security scenario exploring comparisons of Bayesian versus evidential reasoning for dynamic security detection updates. Using the evidential reasoning proportional conflict redistribution (PCR) method, we demonstrate improved trust for dynamically changing detections of denial of service attacks.