  Due to the increased usage of spectrum caused by the exponential growth of
wireless devices, detecting and avoiding interference has become an
increasingly relevant problem to ensure uninterrupted wireless communications.
In this paper, we focus our interest on detecting narrowband interference
caused by signals that despite occupying a small portion of the spectrum only
can cause significant harm to wireless systems, for example, in the case of
interference with pilots and other signals that are used to equalize the effect
of the channel or attain synchronization. Due to the small sizes of these
signals, detection can be difficult due to their low energy footprint, while
greatly impacting (or denying completely in some cases) network communications.
We present a novel narrowband interference detection solution that utilizes
convolutional neural networks (CNNs) to detect and locate these signals with
high accuracy. To demonstrate the effectiveness of our solution, we have built
a prototype that has been tested and validated on a real-world over-the-air
large-scale wireless testbed. Our experimental results show that our solution
is capable of detecting narrowband jamming attacks with an accuracy of up to
99%. Moreover, it is also able to detect multiple attacks affecting several
frequencies at the same time even in the case of previously unseen attack
patterns. Not only can our solution achieve a detection accuracy between 92%
and 99%, but it does so by only adding an inference latency of 0.093ms.
