Machine Learning Tech to Screen Airport Passengers

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The US Homeland Security Department is investing in machine learning technology that could help foreign countries increase airport security at zero cost.

The agency’s Science and Technology Directorate has awarded $200,000 to DataRobot to begin testing an automated machine learning platform that would let airports assess the risks of individual travelers faster and more effectively.

If the prototype proves successful, Homeland Security plans to use the technology to enhance the Global Travel Assessment System (GTAS) run by Customs and Border Protection.

The open-source GTAS application – a passenger data screening and analysis system for enhancing global security –  is designed to help governments around the world that don’t have resources to assess flight risks boost their own airport security to U.S. standards.

Data scientists can vet passengers and predict flight risks of individual travelers.

Anil John, S&T’s program manager for identity management research and development, told nextgov.com that with GTAS, countries can bootstrap advanced risk modeling while avoiding high upfront costs.

However, the software today requires technical expertise, and the rapidly shifting nature of security means models are often out-of-date by the time they’re completed. But automated machine learning could speed up that process and simultaneously make it easier for non-data scientists to find the models that work best for them, John said.

Using DataRobot’s software, analysts can run models against each other to see which ones work best for certain predictions. As an add-on to the GTAS system, the platform could ultimately reduce the technical barriers to using the software and possibly compel more countries to adopt the technology, he said.

Upon completion, the company would test its platform in a wide array of operational scenarios before going to market.