iMIA: Interdependent Mission Impact Assessment Using Subjective Bayesian Networks

Han Jun Yoon, Ashrith Reddy Thukkaraju, Shou Matsumoto, Jair Feldens Ferrari, Donghwan Lee, Myung Kil Ahn, Paulo C. G. Costa, Jin-Hee Cho

Abstract: A mission impact assessment (MIA) framework assesses a mission system’s performance and/or aims to identify risk factors of mission failure to take a mitigation strategy. The iMIA framework, unlike traditional MIA approaches, comprehensively addresses the interdependencies of key system components like asset vulnerabilities, attack behaviors, defense mechanisms, and service/task characteristics. This framework goes beyond conceptual models, providing a validated and detailed MIA framework covering from a conceptual model to detailed mission and system designs. Existing MIA approaches with Bayesian Networks (BNs) overlook inherent uncertainties arising from factors like insufficient evidence or data in real-world applications. To address decision-making challenges in the face of uncertainties, we introduce Subjective Bayesian Networks (SBNs). SBNs estimate uncertainties and interpret them using an expert’s domain knowledge or historical data, forming a subjective opinion through prior belief in SBNs. Using the SBNs, we enhance iMIA’s inference accuracy in a Federated Learning-based mission system (FLMS) for vehicular networks. Our experiments show thatiMIA’s SBN reasoning significantly improves mission outcome inference accuracy compared to conventional BN-based reasoning in existing MIA approaches. We also evaluate mission performance based on service availability and prediction accuracy from the FLMS.