A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs
A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs
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DOI：
10.1145/3468218.3469049
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Jered Pawlak;Yuchen Li;Joshua Price;M. Wright;K. Shamaileh;Quamar Niyaz;V. Devabhaktuni
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文献类型：
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作者：
Jered Pawlak;Yuchen Li;Joshua Price;M. Wright;K. Shamaileh;Quamar Niyaz;V. Devabhaktuni
提出了一种基于机器学习的无人机干扰攻击检测与分类方法。四种攻击类型使用软件定义无线电（SDR）实现;即，阻塞，单音，连续脉冲和协议感知干扰。每种类型都是针对使用正交频分复用（OFDM）通信的无人机发射的，以定性分析其影响，考虑干扰范围，复杂性和严重性。然后，在无人机附近和系统测试场景中使用SDR来记录每次攻击之前和之后的辐射参数。信噪比（SNR），能量阈值，和几个OFDM参数被利用为特征，并馈送到六个ML算法，以探索和实现自主干扰检测/分类。算法的定量评估与度量，包括检测和虚警率，以评估接收到的信号，并促进有效的决策，提高接收的完整性和可靠性。由此产生的ML方法检测和分类干扰的准确率为92.2%，虚警率为1.35%。
In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks on unmanned aerial vehicles (UAVs). Four attack types are implemented using software-defined radio (SDR); namely, barrage, single-tone, successive-pulse, and protocol-aware jamming. Each type is launched against a drone that uses orthogonal frequency division multiplexing (OFDM) communication to qualitatively analyze its impacts considering jamming range, complexity, and severity. Then, an SDR is utilized in proximity to the drone and in systematic testing scenarios to record the radiometric parameters before and after each attack is launched. Signal-to-noise ratio (SNR), energy threshold, and several OFDM parameters are exploited as features and fed to six ML algorithms to explore and enable autonomous jamming detection/classification. The algorithms are quantitatively evaluated with metrics including detection and false alarm rates to evaluate the received signals and facilitate efficient decision-making for improved reception integrity and reliability. The resulting ML approach detects and classifies jamming with an accuracy of 92.2% and a false-alarm rate of 1.35%.