Mapping Interdependencies Among Infrastructures through a Geospatial Software Objects-based Architecture

The proposed approach enables the physical and logical mapping/analysis of network behavior by treating each node in the cyber infrastructure as a logical sensor and relating this information to physical infrastructures using embedded geospatial capabilities. Logical sensors on network nodes collect real-time information and transmit this data via open-geospatial XML formats to control-nodes, similar to approaches used in other solutions such as weather collection where different weather-monitoring systems transmit location and current conditions to a system that uses that data for monitoring and forecasting. Key to this approach is separation of data collection from analysis and use. Data collection must be open and robust to support current and future systems, and also scalable/augmentable to support future growth. To address these requirements we use an open-geospatial XML as a base language to transmit data, metadata and relevant geospatial-information and provide new tools to control sensor-nodes, allowing control units to modify sensor-node behavior for new requirements without replacing the software platform. The system supports analysis functions that "train" over time and learn "baseline" interdependencies of cyber-physical infrastructures so anomalies can be rapidly detected and the system can learn to forecast geographic implications of malfunctions, all the while learning and adapting through experience.