Friend or foeUsing artificial intelligence to predict criminal aircraft

Published 6 March 2018

The ability to forecast criminal activity has been explored to various lengths in science fiction, but does it hold true in reality? It could for U.S. Customs and Border Protection (CBP). ) DHS S&T is developing a Predictive Threat Model (PTM) to help CBP’s Air and Marine Operations Center (AMOC) more quickly and efficiently identify and stop nefarious aircraft.

The ability to forecast criminal activity has been explored to various lengths in science fiction, but does it hold true in reality?

It could for U.S. Customs and Border Protection (CBP).

Currently, the DHS Science and Technology Directorate (S&T) is developing a Predictive Threat Model (PTM) to help CBP’s Air and Marine Operations Center (AMOC) more quickly and efficiently identify and stop nefarious aircraft.

“We can rethink how we do business with the monumental progress this brings to us,” said Tony Crowder, Executive Director at AMOC, looking forward to the impact predictive analytics could have on future operations, “We owe this success to the teamwork of the Domain Awareness Federation and the collaboration within DHS S&T.”

The targets here are small, non-commercial flyers (commercial flight is already highly-regulated). These aircraft could be classified as general aviation (GA) small aircraft, GA jets, ultralights, or even unmanned aircraft systems (UAS).

The skies are filled with increasing numbers of drones and unmanned aircraft with various intents, which can set the stage for potential terror threats, drug smuggling and other illegal activity. CBP’s Detection Enforcement Officers (DEOs) work hard to detect such threats. However, finding the needle in the haystack is an arduous, lengthy process that takes years to effectively master.

S&T notes that in the past, AMOC has been able to interdict criminal aircraft by tracking patterns of suspicious activity.  Criminals would respond by simply adapting their behavior—flying different routes, leaving from different ports, etc. Machine Learning, central to the PTM, would compress the time and effort it takes to respond to a suspicious craft, storing data continuously and allowing operators to more easily understand threat levels in a shorter amount of time.

DEOs identify aircraft using radar, determine where it is coming from, where it might be going, its altitude and other variables if known. If the aircraft is deemed suspect, only then can DEOs start determining how to go about interdicting the aircraft through engagement and coordination with the local law enforcement.