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Title: Real-time Classification of Jamming Attacks against UAVs via on-board Software-defined Radio and Machine Learning-based Receiver Module
In this article, real-time jamming detection against unmanned aerial vehicles (UAVs) is proposed via the integration of a software-defined radio (SDR) with an on-board Raspberry Pi processor. The SDR is utilized for capturing and forwarding the radio frequency signals to a receiver module hosted in the processor. This module extracts signal features characterized by orthogonal frequency division multiplexing (OFDM) parameters, energy parameters, and signal-to-noise ratio (SNR) parameters. Upon feature extraction, the aforementioned module exploits a machine learning (ML) classifier for detecting and classifying four jamming types; namely, barrage, single-tone, successive-pulse, and protocol-aware. The resulting configuration yielded in an overall detection rate (DR) of 93% and a false alarm rate (FAR) of 1.1%, which are in proximity to their counterparts obtained during the validation stage of the receiver module.
Authors:
; ; ; ; ;
Award ID(s):
2006662
Publication Date:
NSF-PAR ID:
10346141
Journal Name:
2022 IEEE International Conference on Electro Information Technology (eIT)
Page Range or eLocation-ID:
252 - 256
Sponsoring Org:
National Science Foundation
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