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：当前最先进的干扰检测技术依赖于链路层指标，例如误码率、数据包投递率、吞吐量以及信噪比的提升。因此，这些技术只能在攻击已摧毁通信链路后进行“事后”检测（即仅在攻击发生之后）。这类解决方案不适用于无人机等移动设备——这类设备可能因无法预判攻击而失去与远程控制器的连接。我们的解决方案基于以下核心思想：当无人机在不知情状态下飞向干扰区域时，其受到的干扰影响（如误码率和信噪比变化）会逐渐增强。因此，无人机可在误码率下降和信噪比提升完全破坏通信链路之前，利用上述现象提前检测干扰。该方法将使无人机及其操控者能够做出明智决策并保持对导航的完全控制，从而增强安全性与可靠性。本文提出Bloodhound+这一适用于低误码率场景下移动设备干扰检测的解决方案。该方法分析从无线信道获取的原始物理层信息（IQ采样数据），将其整合为灰度图像，并采用稀疏自编码器检测由干扰攻击引发的图像异常。为在多种配置下验证方案，我们使用多种硬件、干扰策略和通信参数采集了大规模室内测量数据集。实验结果表明，Bloodhound+在最小可用相对干扰功率下，可检测距干扰源最远20米的室内干扰，最低准确率达99.7%。该方案对干扰机采用的不同采样频率及干扰信号类型均具有鲁棒性。