Authors: Rob Edsall*, Idaho National Laboratory, Ryan Hruska, Idaho National Laboratory, Timothy Klett, Idaho National Laboratory
Topics: Hazards and Vulnerability, Applied Geography, Geographic Information Science and Systems
Keywords: infrastructure, federal, graph database, cyber-physical
Session Type: Poster
Start / End Time: 10:00 AM / 11:40 AM
Room: Napoleon Foyer/Common St. Corridor, Sheraton, 3rd Floor
Presentation File: No File Uploaded
Following incidents such as hurricanes or terror attacks, vital components of the critical lifeline infrastructure (CLI) of the US can suffer lengthy disruptions in service, not only because of impairments on site, but also because of the compromise of other facilities on which the component is dependent. These compromises may be physical (floods, fires) or cyber (denial of-service, control-system-malware attacks) that can propagate through a physical system. A refinery, for example, that is otherwise undamaged may malfunction because it relies on steam generated at a nearby power plant that has been compromised by a cyber attack. These dependencies and relationships are often known to local engineers and operators but not to federal officials and agencies tasked with mitigating losses or restoring operations after the event. The important task of compiling dependency data is difficult and time consuming for infrastructure analysts; Idaho National Laboratory research has sought to facilitate these efforts to “wire up” the CLI of the US. We have developed and operationalized a framework known as AHA that directly addresses the difficulty of dependency analysis by providing analysts and decision makers the capabilities to quickly build robust documentation of infrastructure assets and dependencies, evaluate and understand critical dependencies, and reveal the potential impacts of hazards on critical infrastructure. Our poster will present the framework with focus on the relevance of geography to its implementation, both in the logic of building the dependency data sets as well as in its support of visualization and insight generation for dependency analysis.