Jamming Detection in Low-BER Mobile Indoor Scenarios via Deep Learning

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 signal-to-noise ratio (SNR). As a result, these techniques can only detect jamming 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 abovementioned phenomenon to detect jamming before the increase of the BER and the decrease 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 broad 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.