TSA rolls dice on risk model

Boeing to build Monte Carlo simulation to weigh air transportation risks

Boeing has pushed the Monte Carlo method forward.

Irochka Tischenko

Sometimes, useful homeland security information technology can arise in unexpected places.

For example, the Transportation Security Administration is looking to a computer-
dependent design method used in airframe manufacturing as a template for evaluating air travel risks.

TSA, a Homeland Security Department agency, has recently announced plans to enter into a no-fee agreement with Boeing, under which the company would build a Risk Management Assessment Tool, or RMAT, for the commercial aviation system using what's known as the Monte Carlo model.

The agreement continues work Boeing has had under way since 2004 to help TSA and other aviation industry stakeholders evaluate the costs and benefits of proposed security measures.

The agency announcemed the agreement on the FedBizOpps Web site.

TSA said Boeing would use its Monte Carlo simulation model 'to identify U.S. commercial aviation system vulnerabilities against a wide variety of attack scenarios.'

The Monte Carlo method refers to several ways of using randomly generated numbers fed into a computer simulation many times to estimate the likelihood of an event, specialists in the field say.

The Monte Carlo method plays an important role in many statistical techniques used to characterize risks, such as the probabilistic risk analysis approach used to evaluate possible problems at a nuclear power plant and their consequences.

Boeing engineers have pushed the mathematical usefulness of the Monte Carlo method forward largely by applying the technique to evaluating the risks and consequences of aircraft component failures.

A DHS source said the work of the U.S. Commercial Aviation Partnership, a group of government and industry organizations, had made TSA officials aware of the potential applicability of the Monte Carlo method to building an RMAT for the air travel system.

A paper by four Boeing technologists and a TSA official describing the RMAT model appeared recently in Interfaces, a scholarly journal covering operations research.

The RMAT model uses Monte Carlo analysis along with forms of statistics and calculus drawn from two types of computationally intensive disciplines: system dynamics and econometrics, or mathematical economic analysis. It also applies the principles of linear and nonlinear programming, which are analytic tools used in operations research.

Operations research as a whole, which can be a catchall term for various types of simulation methods, found one of its first federal applications in the job of predicting when and where enemy submarines would strike convoys crossing the Atlantic Ocean during World War II.

During the past three years, 'the government has considered the model results in determining policies for screening and credentialing airport employees, screening passengers and cargo, determining security staffing levels, and charging security fees,' the article states.

Odds of further use

The Boeing and TSA team that crafted the model said that because of the Monte Carlo method's success, the agency is considering extending its use to the analysis of policy problems outside the realm of security.

As for operations research's role in the Battle of the Atlantic, the Navy used it not only to help find submarines but also as a public relations smokescreen to hide the more effective mathematical tool used to sink subs: Cryptanalysis of German naval radio traffic.

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