Model-Based Systems Architecting with Decision Quantification for Cybersecurity, Cost, and Performance

Abstract

The architecture selection process early in a major system acquisition is a critical step in determining the success of a program. There are recognized deficiencies that frequently occur in this step such as poor transparency into the final selection decision and excessive focus on lowest cost, which does not necessarily result in best value. This research investigates improvements to this process by integrating Model-Based Systems Engineering (MBSE) techniques; enforcing rigorous, quantitative evaluation metrics with a corresponding understanding of uncertainties; and eliciting stakeholder feedback in order to generate an architecture that is better optimized and trusted to provide improved value for the stakeholders. The proposed methodology presents a decision authority with an integrated assessment of architecture alternatives, to include expected performance evaluated against desired parameters with corresponding uncertainty distributions, and traceable to the concerns of the system's stakeholders. This thus enables a more informed and objective selection of the preferred alternative. We present a case study that analyzes the evaluation of a service-oriented architecture (SOA) providing satellite command and control with cyber security protections. This serves to define and demonstrate a new, more transparent and trusted architecture selection process, and the results show that it consistently achieves the desired improvements. Several excursions are also presented to show how rigorously capturing uncertainty could potentially lead to greater insights in architecture evaluation, which is a robust area for further investigation. The primary contribution of this research then is improved decision support to an architecture selection in the early phases of a system acquisition program.