Abstract: A system and method for systematically undertaking model-based security analysis of a cyber physical system (CPS) is provided. In one example, a cyber model simulation and a control system simulation are mapped using various methods to determine which portions of the cyber-model simulation and the control system simulation are correlated with one another. Using the determined correlation, when a cyber-attack is generated on the cyber model simulation, a corresponding attack hook can be generated for the control system model. The attack hook is configured to be integrated into the control system model so as to mimic the effect on the control system that a cyber-attack can engender. Once one or more attack hooks are generated, the user can place the hooks into the control system simulation schemas and run a series of simulations to determine the effects of a cyber event on the control system in a CPS.

Abstract: A system and method for systematically undertaking model-based security analysis of a cyber physical system (CPS) is provided. In one example, a cyber model simulation and a control system simulation are mapped using various methods to determine which portions of the cyber-model simulation and the control system simulation are correlated with one another. Using the determined correlation, when a cyber-attack is generated on the cyber model simulation, a corresponding attack hook can be generated for the control system model. The attack hook is configured to be integrated into the control system model so as to mimic the effect on the control system that a cyber-attack can engender. Once one or more attack hooks are generated, the user can place the hooks into the control system simulation schemas and run a series of simulations to determine the effects of a cyber event on the control system in a CPS.

Abstract: Tools and methods for schema matching that generate schema graphs, populate match matrices and display the schema graphs and the match matrices. These tools and methods characterize potential matches between disparate schemata in terms of both a strength of evidence indicating the potential match and an amount of evidence indicating the potential match. A number of match voters generate a set of match scores for each potential match, and these match scores are combined by a vote merger to form a single confidence value for each potential match. A number of filters display the confidence value for each potential match as a link on a graphical user interface. Machine-learning techniques may be employed to adaptively determine confidence values based on previously established matches.