Who Touched My Mission: Towards Probabilistic Mission Impact Assessment

Cyber attacks inevitably generate impacts towards relevant missions. However, concrete methods to accurately evaluate such impacts are rare. In this paper, we propose a probabilistic approach based on Bayesian networks for quantitative mission impact assessment. A System Object Dependency Graph (SODG) is first built to capture the intrusion propagation process at the low operating system level. On top of the SODG, a mission-task-asset (MTA) map can be established to associate the system objects with corresponding tasks and missions. Based on the MTA map, a Bayesian network can be constructed to leverage the collected intrusion evidence and infer the probabilities of tasks and missions being tainted. An example MTA-based BN is provided to show how our approach can enable effective quantitative mission impact assessment.