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DHS S&T Develops AI-Based Method for Aircraft Threat Detection

The Department of Homeland Security‘s science and technology directorate has begun to develop an artificial intelligence-based method for Customs and Border Protection agents to identify non-commercial aircraft threats.

DHS said Thursday its S&T directorate is developing the Predictive Threat Model to aid CBP’s Air and Marine Operations Center in efforts to track small general aviation aircraft, ultralights, jets and unmanned aircraft that may be involved in illegal activities.

Geoffrey Berlin leads the development of this new method that makes use of machine learning and big data applications based on multiple sources of previously recorded aircraft profiles.

PTM aims to use multiple pieces of information to generate a bigger picture of an aerial vehicle’s intentions, DHS noted.

The model is designed to increase the speed and accuracy of AMOC’s threat detection capacities and supplement the center’s 30 years of drug interdiction experience.

S&T has completed the system’s initial prototype and is scheduled to finish the operational phase in six to nine months.

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