Medical Differential Diagnosis (MDD) as the architectural framework for a knowledge model: A vulnerability detection and threat identification methodology for cyber-crime and cyber-terrorism

This research addresses a real world cyberspace problem, where currently no cross industry standard methodology exists. The goal is to develop a model for identification and detection of vulnerabilities and threats of cyber-crime or cyber-terrorism where cyber-technology is the vehicle to commit the criminal or terrorist act (CVCT). This goal was executed through the creation of a CVCT Knowledge Model (KML) methodology. The product was built as a proof of concept and is the first aspect of what could be considered a strategic technical model.;The research for the development and testing of a CVCT Knowledge Model methodology integrates components from three disciplines as a way to investigate a strategic, structural and reproducible knowledge focused methodology and applies it to problem domains in the intelligence community (IC). The three disciples drawn upon are medical differential diagnosis (MDD), the use of knowledge architectural modeling from knowledge management, and components of risk reduction: vulnerability and threat recognition for reduction. In collaboration with credible medical professionals, this research analyzes, reviews, and tests the accuracy of a research developed Medical Differential Diagnosis (MDD) proof-of-concept KML, showing how a physician provides an accurate diagnosis through critical steps. The knowledge factors in the model represent the base technical architecture in the design of this research product, a CVCT Knowledge Model Methodology for vulnerability detection and threat identification.;It is hoped that the Intelligence Community (IC), Department of Defense (DOD) and supportive industry leaders implement this proposed KML methodology as a way to more effectively address CVCT and other problem domains-of-interest.