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Subjective Bayesian Network-based Interdependent Mission Impact Assessment with Game-Theoretic Attack-Defense Interactions
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  • Han Jun Yoon ,
  • Ashrith Reddy Thukkaraju ,
  • Shou Matsumoto ,
  • Jair Feldens Ferrari ,
  • Dongwhan Lee ,
  • Myung Kil Ahn ,
  • Paulo Costa ,
  • Jin-Hee Cho
Han Jun Yoon
Virginia Tech, Virginia Tech

Corresponding Author:[email protected]

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Ashrith Reddy Thukkaraju
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Shou Matsumoto
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Jair Feldens Ferrari
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Dongwhan Lee
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Myung Kil Ahn
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Paulo Costa
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Jin-Hee Cho
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An accurate assessment of a mission system’s performance, called Mission Impact Assessment (MIA), can effectively identify and counteract any issues in the current system to avoid potential risks or vulnerabilities that may cause mission failure. Although the importance of MIA research has been recognized to mitigate the system’s potential risk for more than a decade, little research has shown a comprehensive MIA framework with experimental validation showing the inference performance of the MIA tools under realistic attack-defense interactions. To fill this gap, we propose an interdependent mission impact assessment (MIA), called iMIA, that can adequately capture the inter-relationships of the key components in a mission system and environment and accurately infer a mission outcome (i.e., success or failure) under uncertainty.  In the proposed iMIA, we first consider Subjective Bayesian Networks to evaluate how successfully a given system introduces impacts on mission success under uncertainty due to a lack of evidence.  In addition, we consider strategic attack-defense interactions based on a hypergame theory in which the attacker and defender can take actions under their perceived uncertainty.  Our extensive experiments showed the outperformance of our proposed MIA up to 70% and 25% over the performance of the baseline and the state-of-the-art Bayesian Network-based counterparts, respectively, in terms of inference accuracy in mission performance (e.g., mission outcomes, such as mission success or failure).  We also provide in-depth sensitivity analyses to identify the key system components that should be considered more carefully to avoid mission failure.