Jamming Detection and Classification in OFDM-Based UAVs via Feature- and Spectrogram-Tailored Machine Learning
Yuchen Li, Jered Pawlak, Joshua Price, Khair Al Shamaileh, Quamar Niyaz, Sidike Paheding, Vijay Devabhaktuni
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature-based</i> classification model via conventional ML algorithms. Furthermore, spectrogram images collected following the same testing procedure are exploited to build a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectrogram-based</i> 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.