Document Type
Article
Publication Date
2-8-2022
Department
Department of Applied Computing
Abstract
In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), energy threshold, and key OFDM parameters are used to develop a feature-based classification model via conventional ML algorithms. Furthermore, spectrogram images collected following the same testing procedure are exploited to build a spectrogram-based classification model via state-of-the-art deep learning algorithms (i.e., convolutional neural networks). The performance of both types of algorithms is analyzed quantitatively with metrics including detection and false alarm rates. Results show that the spectrogram-based model classifies jamming with an accuracy of 99.79% and a false-alarm of 0.03%, in comparison to 92.20% and 1.35%, respectively, with the feature-based counterpart.
Publication Title
IEEE Access
Recommended Citation
Li, Y.,
Pawlak, J.,
Price, J.,
Al Shamaileh, K.,
Niyaz, Q.,
Paheding, S.,
&
Devabhaktuni, V.
(2022).
Jamming Detection and Classification in OFDM-based UAVs via Feature- and Spectrogram-tailored Machine Learning.
IEEE Access,
10, 16859-16870.
http://doi.org/10.1109/ACCESS.2022.3150020
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15739
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2022. CCBY - IEEE is not the copyright holder of this material. Please follow the instructions via https://creativecommons.org/licenses/by/4.0/ to obtain full-text articles and stipulations in the API documentation. Publisher’s version of record: https://doi.org/10.1109/ACCESS.2022.3150020