Due to the increased usage of spectrum caused by the exponential growth of wireless devices, detecting and avoiding interference has become an increasingly relevant problem to ensure uninterrupted wireless communications. In this paper, we focus our interest on detecting narrowband interference caused by signals that despite occupying a small portion of the spectrum only can cause significant harm to wireless systems, for example, in the case of interference with pilots and other signals that are used to equalize the effect of the channel or attain synchronization. Due to the small sizes of these signals, detection can be difficult due to their low energy footprint, while greatly impacting (or denying completely in some cases) network communications. We present a novel narrowband interference detection solution that utilizes convolutional neural networks (CNNs) to detect and locate these signals with high accuracy. To demonstrate the effectiveness of our solution, we have built a prototype that has been tested and validated on a real-world over-the-air large-scale wireless testbed. Our experimental results show that our solution is capable of detecting narrowband jamming attacks with an accuracy of up to 99%. Moreover, it is also able to detect multiple attacks affecting several frequencies at the same time even in the case of previously unseen attack patterns. Not only can our solution achieve a detection accuracy between 92% and 99%, but it does so by only adding an inference latency of 0.093ms.
翻译:由于无线装置的指数式增长导致频谱的使用增加,探测和避免干扰已成为一个越来越重要的问题,以确保无线通信的无线通信。在本文件中,我们关注的焦点是检测信号所造成的窄带干扰,这些信号尽管占据了一小部分频谱,但只能对无线系统造成重大伤害,例如,对飞行员的干扰和用于平衡频道效应或实现同步的其他信号。由于这些信号的大小较小,探测可能由于它们的能量足迹低而变得困难,同时对网络通信产生极大影响(或在某些情况下完全否认)。我们提出了一个新型的窄带干扰探测解决方案,利用革命性神经网络(CNNs)来非常准确地探测和定位这些信号。为了证明我们的解决办法的有效性,我们建立了一个模型,在现实世界的超空大型无线测试台已经测试和验证。我们的实验结果表明,我们的解决办法能够以99 %的精确度探测到窄带干扰攻击。此外,我们还能够探测到多个频率受到影响的窄带干扰攻击,即使是在99 %的精确度之间也只能在99 %的精确度中探测到一些频率。