Jamming Detection and Classification in OFDM-Based UAVs via Feature- and Spectrogram-Tailored Machine Learning
Jamming Detection and Classification in OFDM-Based UAVs via Feature- and Spectrogram-Tailored Machine Learning
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DOI：
10.1109/access.2022.3150020
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Yuchen Li;Jered Pawlak;Joshua Price;K. A. Al Shamaileh;Quamar Niyaz;Sidike Paheding;V. Devabhaktuni-V.-Devabhakt
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文献类型：
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作者：
Yuchen Li;Jered Pawlak;Joshua Price;K. A. Al Shamaileh;Quamar Niyaz;Sidike Paheding;V. Devabhaktuni-V.-Devabhakt
本文提出了一种机器学习（ML）方法来检测和分类针对正交频分复用（OFDM）接收器的干扰攻击，并应用于无人机（UAV）。使用软件定义无线电（SDR），四种类型的干扰攻击；即，启动并调查弹幕、协议感知、单音和连续脉冲。考虑干扰范围、发射复杂性和攻击严重性，对每种类型进行定性评估。然后，通过将 SDR 放置在无人机（即无人机）附近来建立系统测试程序，以在干扰攻击发起之前和之后提取辐射特征。包括信噪比 (SNR)、能量阈值和关键 OFDM 参数在内的数值特征用于通过传统 ML 算法开发基于特征的分类模型。此外，按照相同的测试程序收集的频谱图图像可通过最先进的深度学习算法（即卷积神经网络）构建基于频谱图的分类模型。通过检测率和误报率等指标对两种算法的性能进行定量分析。结果表明，基于频谱图的模型对干扰进行分类的准确度为 99.79%，误报率为 0.03%，而基于特征的对应模型的分类准确度分别为 92.20% 和 1.35%。
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.