Machine Learning Model Tracks U.S. Spy Planes Posted on : Aug 12 - 2017

Among the tasks you can train a computer to perform is scanning the skies over the U.S. for the alarming number of surveillance and spy aircraft.

The news web site BuzzFeed did just that, reporting this week that it employed a machine-learning algorithm to first recognize known spy planes, and then combine that model with a large set of flight-tracking data from a commercial web site.

The AI project mapped thousands of surveillance flights operated by federal agencies over a four-month period, including a military contractor tracking terrorists in Africa that is also flying surveillance aircraft over U.S. cities, BuzzFeed reported.

Reporters-turned-data-scientists started by making calculations describing the flight characteristics of about 20,000 aircraft contained in a database called Flightradar24, including altitude, speed, flight duration and—most important, it emerged—turning rates.

Flightradar24 gathers data from a network of ground-based receivers supplemented by Federal Aviation Administration receivers. The ground radars sweep up a flight data transmitted by aircraft transponders, including unique identifiers for each plane.

The aerial gumshoes then used an algorithm called Random Forest (referred to on Github as randomForest, Random Forests, random-forest and variations of those names). They trained the algorithm, popular among data scientists for its classification capabilities, to spot characteristics of about 100 known FBI and Department of Homeland Security surveillance aircraft, mixed in with about 500 randomly selected aircraft.

(According to a GitHub report, the investigators filtered flight data to remove aircraft registered abroad, commercial aircraft and planes with less than 500 transponder pings.)

The algorithm was then trained to determine which data was most significant. “Given that spy planes tend to fly in tight circles, it put most weight on the planes’ turning rates,” the web site reported. View More