  Jamming attacks target a wireless network creating an unwanted denial of
service. 5G is vulnerable to these attacks despite its resilience prompted by
the use of millimeter wave bands. Over the last decade, several types of
jamming detection techniques have been proposed, including fuzzy logic, game
theory, channel surfing, and time series. Most of these techniques are
inefficient in detecting smart jammers. Thus, there is a great need for
efficient and fast jamming detection techniques with high accuracy. In this
paper, we compare the efficiency of several machine learning models in
detecting jamming signals. We investigated the types of signal features that
identify jamming signals, and generated a large dataset using these parameters.
Using this dataset, the machine learning algorithms were trained, evaluated,
and tested. These algorithms are random forest, support vector machine, and
neural network. The performance of these algorithms was evaluated and compared
using the probability of detection, probability of false alarm, probability of
miss detection, and accuracy. The simulation results show that jamming
detection based random forest algorithm can detect jammers with a high
accuracy, high detection probability and low probability of false alarm.
