The events of 9/11 led the NASA Glenn Research Center to introduce a security component to its Aviation Safety Program, with a robust portfolio of advanced technologies designed to make flight more secure. The portfolio needed to be prioritized and managed to ensure that investment payoffs were maximized in the most promising technologies. In addition, an assessment was desired to identify the integration issues that the technologies could have with each other as well as within the overall National Airspace System (NAS).
To accomplish this, NASA needed a new approach. A complete vulnerability assessment of the NAS would have to be performed to formulate a baseline risk so that NASA technologies could be applied to find how much each technology reduced risk. This enormous task was performed through the use of a computational methodology called logic-evolved decision (LED) analysis, which was developed at the Los Alamos National Laboratory for modeling the behavior of complex systems with respect to decision-support applications. The LED computational algorithm uses linked, formal logic models to represent the basic functions of a decision analysis tool. LED incorporates fuzzy logic, approximate reasoning, possibility, probability, multi-attribute scoring, and graph theory to construct decision-support models. It is a flexible, self-contained, comprehensive, and traceable software tool for risk-based prioritization and portfolio management across a broad spectrum of applications.
Using the LED tool, in 2006 researchers from the NASA Glenn Research Center and the NASA Langley Research Center developed a comprehensive set of several million attack scenarios, using fault tree analysis to define terrorist threats to aircraft, airports, and the airspace. Then a much smaller, representative set of attacks were chosen to apply the technologies to determine the effective risk reduction. Technology readiness levels, technical development risks, implementation risks, cultural and certification issues, and cost-to-benefit ratios were considered. The LED methodology allowed the researchers to use approximate reasoning to construct inference models for analyzing each attack scenario. Expertise from NASA technologists, experts in all aspects of aviation operations and security, and national security analysts was input to the attack scenarios and inference models to define the threats, the security technologies, and the impact of the technologies. By applying these inference models to the attack scenarios, the researchers could determine the final risk and compare it with the baseline risks to determine the effective risk reduction. Risk-reduction technologies were analyzed both as standalone operations and coupled with one or more other technologies for increased risk reduction.
The objective of these assessments was to provide a decision-support tool for prioritizing NASA research in aviation security. This top-down analysis and modeling approach utilized LED methodologies and techniques to rank order proposed NASA security research projects according to their effectiveness in reducing risk.
Glenn contact: Kenneth L. Fisher, 216-433-5655, Kenneth.L.Fisher@nasa.govLast updated: August 24, 2007
Responsible NASA Official:
Gynelle.C.Steele@nasa.gov
216-433-8258
Point of contact for NASA Glenn's Research & Technology reports:
Cynthia.L.Dreibelbis@nasa.gov
216-433-2912
SGT, Inc.
Web page curator:
Nancy.L.Obryan@nasa.gov
216-433-5793
Wyle Information Systems, LLC
