Big data analytics nuclear security framework

Abstract

Abstract Nuclear security is defined as the prevention and detection of, and response to, theft, sabotage, unauthorized access, illegal transfer, or other malicious acts involving nuclear material, other radioactive substances, or their associated facilities. Whereas, big data analytics is denoted as a new way of collecting, analyzing large amounts of data, finding the appropriate patterns to support decision making, hence improve the action taken in solving the problems. The problem with the nuclear security system in most of the nuclear organizations and nuclear facilities in the world today is that the unintegrated data management in nuclear security systems is causing sub-optimal efficiency in the detection of malicious acts involving nuclear/radioactive materials. We argue that a big data analytics framework for nuclear security should be created and utilized to integrate all data in nuclear security systems as well as serving for needs of intelligence especially involving predictive analytics, both within organizations and inter-organization. Therefore, in this paper, we present a conceptual framework of big data analytics for nuclear security. The framework is formulated based on a holistic methodology with the aims to integrate all the data of the nuclear security systems at the organization/facilities level and national level so that the data could be analyzed to derive the appropriate patterns, hence increase the accuracy of decision making that could lead to increased efficiency of detection process and response to the nuclear security event.