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Detection and Classification of Radio
Frequency Jamming Attacks using Machine learning 1Division
of Information Technology, Netaji Subhas Institute of Technology University of Delhi, New Delhi, India {kasturi710,
a.j120562, jagdeepknit}@gmail.com Abstract Wireless networks are an important aspect of
communication technologies that avoid the cost and burden of cable
installation. They play a vital role in our everyday lives. However, these
wireless networks have some limitations which can be exploited by malicious
users to capture transmitted information or cause disruptions in
communications. A Radio Frequency Jamming (RF-Jamming) attack is one such
type of attack that interferes with authentic wireless signals by reducing
the signal-to-noise ratio. These types of attacks pose serious threats to
many applications especially the safety critical ones such as self-driving
cars. Hence, it is crucial to institute countermeasures to prevent these
attacks and establish a reliable communication system. Furthermore, to take
the appropriate steps for the protection against such attacks, it is
important to know the type of jamming attack that a network has been exposed
to. In other words, in addition to detection, the classification of these
attacks is also necessary. Therefore, in this paper, we tackle this problem
and propose a machine learning-based classification technique for different
types of jamming attacks. We simulate the jamming scenario in wireless ad-hoc
networks using the network simulator ns-3 and use the data collected from the
simulation to train and evaluate different algorithms. We compare the
accuracy of each algorithm and provide the results that showcase that the
classification of jamming attacks can be done with very high accuracy using
the Gradient Boosting Algorithm. Keywords: Jamming Attacks Classification, Wireless
Networks, NS-3, Gradient Boosting +: Corresponding author: Jagdeep Singh Journal of Wireless Mobile Networks, Ubiquitous
Computing, and Dependable Applications (JoWUA),
Vol. 11, No. 4, pp. 49-62, December 2020 [pdf] DOI: 10.22667/JOWUA.2020.12.31.049 |