The current state of the art on jamming detection relies on link-layer metrics. A few examples are the bit-error-rate (BER), the packet delivery ratio, the throughput, and the increase in the signal-to-noise ratio (SNR). As a result, these techniques can only detect jamming \emph{ex-post}, i.e., once the attack has already taken down the communication link. These solutions are unfit for mobile devices, e.g., drones, which might lose the connection to the remote controller, being unable to predict the attack. Our solution is rooted in the idea that a drone unknowingly flying toward a jammed area is experiencing an increasing effect of the jamming, e.g., in terms of BER and SNR. Therefore, drones might use the above-mentioned phenomenon to detect jamming before the decrease of the BER and the increase of the SNR completely disrupt the communication link. Such an approach would allow drones and their pilots to make informed decisions and maintain complete control of navigation, enhancing security and safety. This paper proposes Bloodhound+, a solution for jamming detection on mobile devices in low-BER regimes. Our approach analyzes raw physical-layer information (I-Q samples) acquired from the wireless channel. We assemble this information into grayscale images and use sparse autoencoders to detect image anomalies caused by jamming attacks. To test our solution against a wide set of configurations, we acquired a large dataset of indoor measurements using multiple hardware, jamming strategies, and communication parameters. Our results indicate that Bloodhound+ can detect indoor jamming up to 20 meters from the jamming source at the minimum available relative jamming power, with a minimum accuracy of 99.7\%. Our solution is also robust to various sampling rates adopted by the jammer and to the type of signal used for jamming.

翻译：当前干扰检测的最新技术依赖于链路层指标，例如误码率、数据包投递率、吞吐量以及信噪比。因此，这些方法只能在攻击已导致通信链路中断后，即事后检测干扰。这类方案不适用于移动设备（如无人机），因为这可能使无人机失去与遥控器的连接，无法预测攻击。我们的解决方案基于这样的理念：无人机在未意识到的情况下飞向干扰区域时，会经历干扰效应的持续增强（如误码率和信噪比的变化）。因此，无人机可利用上述现象，在误码率下降和信噪比升高完全破坏通信链路之前检测干扰。该方法可使无人机及其操控者做出明智决策并保持完全导航控制权，从而提升安全性与可靠性。本文提出Bloodhound+——一种面向低误码率场景下移动设备的干扰检测方案。该方法分析从无线信道获取的原始物理层信息（I-Q采样点），将其整合为灰度图像，并利用稀疏自编码器检测由干扰攻击引起的图像异常。为在多种配置下测试方案，我们使用多种硬件、干扰策略和通信参数采集了大量室内测量数据集。实验结果表明，在最小可用相对干扰功率条件下，Bloodhound+可在距干扰源20米内实现室内干扰检测，最低准确率达99.7%。该方案对干扰方采用的不同采样速率及干扰信号类型均具有鲁棒性。