  Safety and security issues for Critical Infrastructures are growing as
attackers adopt drones as an attack vector flying in sensitive airspaces, such
as airports, military bases, city centers, and crowded places. Despite the use
of UAVs for logistics, shipping recreation activities, and commercial
applications, their usage poses severe concerns to operators due to the
violations and the invasions of the restricted airspaces. A cost-effective and
real-time framework is needed to detect the presence of drones in such cases.
In this contribution, we propose an efficient radio frequency-based detection
framework called URANUS. We leverage real-time data provided by the Radio
Frequency/Direction Finding system, and radars in order to detect, classify and
identify drones (multi-copter and fixed-wings) invading no-drone zones. We
adopt a Multilayer Perceptron neural network to identify and classify UAVs in
real-time, with $90$% accuracy. For the tracking task, we use a Random Forest
model to predict the position of a drone with an MSE $\approx0.29$, MAE
$\approx0.04$, and $R^2\approx 0.93$. Furthermore, coordinate regression is
performed using Universal Transverse Mercator coordinates to ensure high
accuracy. Our analysis shows that URANUS is an ideal framework for identifying,
classifying, and tracking UAVs that most Critical Infrastructure operators can
adopt.
